Download or read book MOVING OBJECT DETECTION BASED ON BACKGROUND SUBTRACTION UNDER CWT DOMAIN FOR VIDEO SURVEILLANCE SYSTEM written by Chandra Shaker Arrabotu and published by Archers & Elevators Publishing House. This book was released on with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Moving Object Detection Using Background Subtraction Algorithms written by Priyank Shah and published by . This book was released on 2014-06-30 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2014 in the subject Computer Science - Miscellaneous, grade: 9.2, language: English, abstract: In this thesis we present an operational computer video system for moving object detection and tracking . The system captures monocular frames of background as well as moving object and to detect tracking and identifies those moving objects. An approach to statistically modeling of moving object developed using Background Subtraction Algorithms. There are many methods proposed for Background Subtraction algorithm in past years. Background subtraction algorithm is widely used for real time moving object detection in video surveillance system. In this paper we have studied and implemented different types of methods used for segmentation in Background subtraction algorithm with static camera. This paper gives good understanding about procedure to obtain foreground using existing common methods of Background Subtraction, their complexity, utility and also provide basics which will useful to improve performance in the future . First, we have explained the basic steps and procedure used in vision based moving object detection. Then, we have debriefed the common methods of background subtraction like Simple method, statistical methods like Mean and Median filter, Frame Differencing and W4 System method, Running Gaussian Average and Gaussian Mixture Model and last is Eigenbackground Model. After that we have implemented all the above techniques on MATLAB software and show some experimental results for the same and compare them in terms of speed and complexity criteria. Also we have improved one of the GMM algorithm by combining it with optical flow method, which is also good method to detect moving elements.

Download or read book Background Modeling and Foreground Detection for Video Surveillance written by Thierry Bouwmans and published by CRC Press. This book was released on 2014-07-25 with total page 633 pages. Available in PDF, EPUB and Kindle. Book excerpt: Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements.Incorporating both establish

Download or read book Object Detection Using Motion Or Sound Sensing written by Harikrishna Muriki and published by . This book was released on 2011 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The main purpose of this work is to implement a new framework for the detection of activities based on the temporal difference method. This system mainly consists of a unique interface with an integrated camera and microphone, for the purpose of monitoring moving objects and sound respectively. The proposed system also detracts one common flaw in motion detection based on the frame differencing technique, with the fusion of background subtraction technique and frame difference method. With the ever increasingly heightened sense of safety consciousness in today's society, video surveillance systems have been widely used in several fields, such as military affairs, public space security monitoring, and even in some private homes. The detection of the occurrence of activities is the most basic and important part of video surveillance systems, as such its quality and robustness warrant special attention and continuous research. The proposed system was implemented using MATLAB 7.10.0, and the results are found to be effective and robust.

Download or read book Video based Motion Detection for Stationary and Moving Cameras written by Rui Wang and published by . This book was released on 2014 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: In real world monitoring applications, moving object detection remains to be a challenging task due to factors such as background clutter and motion, illumination variations, weather conditions, noise, and occlusions. As a fundamental first step in many computer vision applications such as object tracking, behavior understanding, object or event recognition, and automated video surveillance, various motion detection algorithms have been developed ranging from simple approaches to more sophisticated ones. In this thesis, we present two moving object detection frameworks. The first framework is designed for robust detection of moving and static objects in videos acquired from stationary cameras. This method exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison. This hybrid system can handle challenges such as shadows, illumination changes, dynamic background, stopped and removed objects. Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms most state-of-the-art methods. The second framework adapts moving object detection to full motion videos acquired from moving airborne platforms. This framework has two main modules. The first module stabilizes the video with respect to a set of base-frames in the sequence. The stabilization is done by estimating four-point homographies using prominent feature (PF) block matching, motion filtering and RANSAC for robust matching. Once the frame to base frame homographies are available the flux tensor motion detection module using local second derivative information is applied to detect moving salient features. Spurious responses from the frame boundaries and other post- processing operations are applied to reduce the false alarms and produce accurate moving blob regions that will be useful for tracking.

Download or read book Detection and Classification of Moving Objects for an Automated Surveillance System written by and published by . This book was released on 2006 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Video Surveillance written by Weiyao Lin and published by BoD – Books on Demand. This book was released on 2011-02-03 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest achievements and developments in the field of video surveillance. The chapters selected for this book comprise a cross-section of topics that reflect a variety of perspectives and disciplinary backgrounds. Besides the introduction of new achievements in video surveillance, this book also presents some good overviews of the state-of-the-art technologies as well as some interesting advanced topics related to video surveillance. Summing up the wide range of issues presented in the book, it can be addressed to a quite broad audience, including both academic researchers and practitioners in halls of industries interested in scheduling theory and its applications. I believe this book can provide a clear picture of the current research status in the area of video surveillance and can also encourage the development of new achievements in this field.

Download or read book Learning Fully Convolutional Networks for Background Subtraction in Surveillance Videos written by and published by . This book was released on 2019 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extracting foreground regions can provide contextual information for a variety of computer vision tasks, including object detection, visual tracking, semantic segmentation etc., in surveillance systems. Traditional methods in the literature suffer from multiple challenges such as background clusters, objects overlapping in the visual field, shadows, lighting changes, fast-moving objects, and objects being introduced or removed from the scene. To address these issues, this work presents a learning-based method for subtracting background regions in individual video frames. The proposed method utilizes the recently developed fully convolutional networks (FCNs), which take input of arbitrary size and produce correspondingly-sized output. The network trained end-to end, pixel-to-pixel, was able to predict the foreground pixels with lesser noise and better generalization to the extent that exceeds the result of the traditional methods. Integrating with transfer learning and image pyramids techniques further enhance the stability of the models. The performance of the models was compared for different scenarios.

Download or read book Application of Footstep Sound and Lab Colour Space in Moving Object Detection and Image Quality Measurement Using Opposite Colour Pairs written by Aditya Roshan and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This PhD dissertation is focused on two of the major tasks of an Atlantic Innovation Fund (AIF) sponsored “Triple-sensitive Camera Project”. The first task focuses on the improvement of moving object detection techniques, and second on the evaluation of camera image quality. Cameras are widely used in security, surveillance, site monitoring, traffic, military, robotics, and other applications, where detection of moving objects is critical and important. Information about image quality is essential in moving object detection. Therefore, detection of moving objects and quality evaluation of camera images are two of the critical and challenging tasks of the AIF Triple-sensitive Camera Project. In moving object detection, frame-based and background-based are two major techniques that use a video as a data source. Frame-based techniques use two or more consecutive image frames to detect moving objects, but they only detect the boundaries of moving objects. Background-based techniques use a static background that needs to be updated in order to compensate for light change in a camera scene. Many background modelling techniques involving complex models are available which make the entire procedure very sophisticated and time consuming. In addition, moving object detection techniques need to find a threshold to extract a moving object. Different thresholding methodologies generate varying threshold values which also affect the results of moving object detection. When it comes to quality evaluation of colour images, existing Full Reference methods need a perfect colour image as reference and No-Reference methods use a gray image generated from the colour image to compute image quality. However, itis very challenging to find a perfect reference colour image. When a colour image is converted to a grey image for image quality evaluation, neither colour information nor human colour perception is available for evaluation. As a result, different methods give varying quality outputs of an image and it becomes very challenging to evaluate the quality of colour images based on human vision. In this research, a single moving object detection using frame differencing technique is improved using footstep sound which is produced by the moving object present in camera scene, and background subtraction technique is improved by using opposite colour pairs of Lab colour space and implementing spatial correlation based thresholding techniques. Novel thresholding methodologies consider spatial distribution of pixels in addition to the statistical distribution used by existing methods. Out of four videos captured under different scene conditions used to measure improvements, a specified frame differencing technique shows an improvement of 52% in object detection rate when footstep sound is considered. Other frame-based techniques using Optical flow and Wavelet transform such are also improved by incorporating footstep sound. The background subtraction technique produces better outputs in terms of completeness of a moving object when opposite colour pairs are used with thresholding using spatial autocorrelation techniques. The developed technique outperformed background subtraction techniques with most commonly used thresholding methodologies. For image quality evaluation, a new “No-Reference” image quality measurement technique is developed which evaluates quantitative image quality score as it is evaluated by human eyes. The SCORPIQ technique developed in this research is independent of a reference image, image statistics, and image distortions. Colour segments of an image are spatially analysed using the colour information available in Lab colour space. Quality scores from SCORPIQ technique using LIVE image database yield distinguished results as compared to quality scores from existing methods which give similar results for visually different images. Compared to visual quality scores available with LIVE database, the quality scores from SCORPIQ technique are 3 times more distinquishable. SCORPIQ give 4 to 20 times distinguishable results compared to statistics based results which also does not follow the quality scores as evaluated by human eyes.

Download or read book Video Object Extraction in Distributed Surveillance Systems written by Mohammed Asaad Ghazal and published by . This book was released on 2010 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, automated video surveillance and related video processing algorithms have received considerable attention from the research community. Challenges in video surveillance rise from noise, illumination changes, camera motion, splits and occlusions, complex human behavior, and how to manage extracted surveillance information for delivery, archiving, and retrieval: Many video surveillance systems focus on video object extraction, while few focus on both the system architecture and video object extraction. We focus on both and integrate them to produce an end-to-end system and study the challenges associated with building this system. We propose a scalable, distributed, and real-time video-surveillance system with a novel architecture, indexing, and retrieval. The system consists of three modules: video workstations for processing, control workstations for monitoring, and a server for management and archiving. The proposed system models object features as temporal Gaussians and produces: an 18 frames/second frame-rate for SIF video and static cameras, reduced network and storage usage, and precise retrieval results. It is more scalable and delivers more balanced distributed performance than recent architectures. The first stage of video processing is noise estimation. We propose a method for localizing homogeneity and estimating the additive white Gaussian noise variance, which uses spatially scattered initial seeds and utilizes particle filtering techniques to guide their spatial movement towards homogeneous locations from which the estimation is performed. The noise estimation method reduces the number of measurements required by block-based methods while achieving more accuracy. Next, we segment video objects using a background subtraction technique. We generate the background model online for static cameras using a mixture of Gaussians background maintenance approach. For moving cameras, we use a global motion estimation method offline to bring neighboring frames into the coordinate system of the current frame and we merge them to produce the background model. We track detected objects using a feature-based object tracking method with improved detection and correction of occlusion and split. We detect occlusion and split through the identification of sudden variations in the spatia-temporal features of objects. To detect splits, we analyze the temporal behavior of split objects to discriminate between errors in segmentation and real separation of objects. Both objective and subjective experimental results show the ability of the proposed algorithm to detect and correct both splits and occlusions of objects. For the last stage of video processing, we propose a novel method for the detection of vandalism events which is based on a proposed definition for vandal behaviors recorded on surveillance video sequences. We monitor changes inside a restricted site containing vandalism-prone objects and declare vandalism when an object is detected as leaving the site while there is temporally consistent and significant static changes representing damage, given that the site is normally unchanged after use. The proposed method is tested on sequences showing real and simulated vandal behaviors and it achieves a detection rate of 96%. It detects different forms of vandalism such as graffiti and theft. The proposed end-ta-end video surveillance system aims at realizing the potential of video object extraction in automated surveillance and retrieval by focusing on both video object extraction and the management, delivery, and utilization of the extracted information.

Download or read book Particle Filter Based Intelligent Video Surveillance System written by Ying-Jen Chen and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, an intelligent video surveillance (IVS) system is designed based on the particle filter. The designed IVS system can gather the information of the number of persons in the area and hot spots of the area. At first, the Gaussian mixture background model is utilized to detect moving objects by background subtraction. The moving object appearing in the margin of the video frame is considered as a new person. Then, a new particle filter is assigned to track the new person when it is detected. A particle filter is canceled when the corresponding tracked person leaves the video frame. Moreover, the Kalman filter is utilized to estimate the position of the person when the person is occluded. Information of the number of persons in the area and hot spots is gathered by tracking persons in the video frame. Finally, a user interface is designed to feedback the gathered information to users of the IVS system. By applying the proposed IVS system, the load of security guards can be reduced. Moreover, by hot spot analysis, the business operator can understand customer habits to plan the traffic flow and adjust the product placement for improving customer experience.

Download or read book Exploiting Scene Context for On line Object Tracking in Unconstrained Environments written by Salma Moujtahid and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the increasing need for automated video analysis, visual object tracking became an important task in computer vision. Object tracking is used in a wide range of applications such as surveillance, human-computer interaction, medical imaging or vehicle navigation. A tracking algorithm in unconstrained environments faces multiple challenges : potential changes in object shape and background, lighting, camera motion, and other adverse acquisition conditions. In this setting, classic methods of background subtraction are inadequate, and more discriminative methods of object detection are needed. Moreover, in generic tracking algorithms, the nature of the object is not known a priori. Thus, off-line learned appearance models for specific types of objects such as faces, or pedestrians can not be used. Further, the recent evolution of powerful machine learning techniques enabled the development of new tracking methods that learn the object appearance in an online manner and adapt to the varying constraints in real time, leading to very robust tracking algorithms that can operate in non-stationary environments to some extent. In this thesis, we start from the observation that different tracking algorithms have different strengths and weaknesses depending on the context. To overcome the varying challenges, we show that combining multiple modalities and tracking algorithms can considerably improve the overall tracking performance in unconstrained environments. More concretely, we first introduced a new tracker selection framework using a spatial and temporal coherence criterion. In this algorithm, multiple independent trackers are combined in a parallel manner, each of them using low-level features based on different complementary visual aspects like colour, texture and shape. By recurrently selecting the most suitable tracker, the overall system can switch rapidly between different tracking algorithms with specific appearance models depending on the changes in the video. In the second contribution, the scene context is introduced to the tracker selection. We designed effective visual features, extracted from the scene context to characterise the different image conditions and variations. At each point in time, a classifier is trained based on these features to predict the tracker that will perform best under the given scene conditions. We further improved this context-based framework and proposed an extended version, where the individual trackers are changed and the classifier training is optimised. Finally, we started exploring one interesting perspective that is the use of a Convolutional Neural Network to automatically learn to extract these scene features directly from the input image and predict the most suitable tracker.

Download or read book Event Detection in Surveillance Video written by Ricardo Augusto Castellanos Jimenez and published by . This book was released on 2010 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital video is being used widely in a variety of applications such as entertainment, surveillance and security. Large amount of video in surveillance and security requires systems capable to processing video to automatically detect and recognize events to alleviate the load on humans and enable preventive actions when events are detected. The main objective of this work is the analysis of computer vision techniques and algorithms used to perform automatic detection of events in video sequences. This thesis presents a surveillance system based on optical flow and background subtraction concepts to detect events based on a motion analysis, using an event probability zone definition. Advantages, limitations, capabilities and possible solution alternatives are also discussed. The result is a system capable of detecting events of objects moving in opposing direction to a predefined condition or running in the scene, with precision greater than 50% and recall greater than 80%.

Download or read book Dim Object Tracking in Cluttered Image Sequences written by Kaveh Ahmadi and published by . This book was released on 2016 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research is aimed at developing efficient dim object tracking techniques in cluttered image sequences. In this dissertation, a number of new techniques are presented for image enhancement, super resolution (SR), dim object tracking, and multi-sensor object tracking. Cluttered images are impaired by noise. To deal with a mixed Gaussian and impulse noise in the image, a novel sparse coding super resolution is developed. The sparse coding has recently become a widely used tool in signal and image processing. The sparse linear combination of elements from an appropriately chosen over-complete dictionary can represent many signal patches. The proposed SR is composed of a Genetic Algorithm (GA) search step to find the optimum match from low resolution dictionary. By using GA, the proposed SR is capable of efficiently up-sampling the low resolution images while preserving the image details. Dim object tracking in a heavy clutter environment is a theoretical and technological challenge in the field of image processing. For a small dim object, conventional tracking methods fail for the lack of geometrical information. Multiple Hypotheses Testing (MHT) is one of the generally accepted methods in target tracking systems. However, processing a tree structure with a significant number of branches in MHT has been a challenging issue. Tracking high-speed objects with traditional MHT requires some presumptions which limit the capabilities of these methods. In this dissertation, a hierarchal tracking system in two levels is presented to solve this problem. For each point in the lower-level, a Multi Objective Particle Swarm Optimization (MOPSO) technique is applied to a group of consecutive frames in order to reduce the number of branches in each tracking tree. Thus, an optimum track for each moving object is obtained in a group of frames. In the upper-level, an iterative process is used to connect the matching optimum tracks of the consecutive frames based on the spatial information and fitness values. Another problem of dim object tracking is background subtraction which is difficult due to noisy environment. This dissertation presents a novel algorithm for detecting and tracking small dim targets in Infrared (IR) image sequences with low Signal to Noise Ratio (SNR) based on the frequency and spatial domain information. Using a Dual-Tree Complex Wavelet Transform (DT-CWT), a Constant False Alarm Rate (CFAR) detector is applied in the frequency domain to find potential positions of objects in a frame. Following this step, a Support Vector Machine (SVM) classification is applied to accept or reject each potential point based on the spatial domain information of the frame. The combination of the frequency and spatial domain information demonstrates the high efficiency and accuracy of the proposed method which is supported by the experimental results. One of the important tools applied in this dissertation is Particle Filter (PF). The PF, a nonparametric implementation of the Bayes filter, is commonly used to estimate the state of a dynamic non-linear non-Gaussian system. Despite PF's successful applications, it suffers from sample impoverishment in real world applications. Most of the recent PF based techniques try to improve the functionality of the PF through evolutionary algorithms in the cases of unexpected changes in the system states. However, they have not addressed the discontinuity of observation which is unpreventable in the real world. This dissertation incorporates a recently developed social-spider optimization technique into PF to overcome the drawback of previous methods in these cases. The problem of object tracking using multi-sensor data is a theoretical and technological challenge in the field of image processing which is presented as the final algorithm in this dissertation. Most of the conventional multi-sensor methods fail to track small dim objects in a cluttered background due to the lack of geometrical target information and unexpected large discontinuities in the measurement data. In this dissertation, a multi-sensor Swarm Intelligence Particle Filter (SIPF) is proposed in an environment covered by a set of multiple calibrated sensors with overlapping field of views. The proposed hierarchical method is divided into two levels. In the lower-level, SIPF is applied to locate the targets in each sensor based on the prior information. Each sensor reports the target position and its related fitness value to a dynamically selected central sensor. In the upper-level, the central sensor finds the best of the reported position for each target and broadcasts its position to all sensors at the lower level as the actual position of the target. Experimental results show this method is able to utilize multi-sensor data to produce highly accurate tracks in noisy datasets even in the case of large jumps or discontinuous observations well beyond the conventional tracking methods..

Download or read book Classification of Moving Objects and Recognition of Human Activity Using Infrared Surveillance Sensors written by Jakir Hossen and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear pyroelectric array sensors have enabled useful classifications of objects such as humans and animals to be performed with relatively low cost hardware in border and perimeter security applications. Ongoing research has sought to improve the performance of these sensors through signal processing algorithms. In this thesis, we introduce the use of Hidden Markov Tree (HMT) models for object recognition in images generated by linear pyroelectric sensors. HMTs are trained to statistically model the wavelet features of individual objects through an expectation-maximization (EM) learning process. Human versus animal classification for a test object is made by evaluating its wavelet features against the trained HMTs using the maximum-likelihood (ML) criterion. The classification performance of this approach is compared to two other techniques; a texture, shape, and spectral component feature (TSSF) based classifier and a speeded up robust feature (SURF) based classifier. The evaluation indicates that among the three techniques, the wavelet based HMT model works well, is robust, and has improved classification performance compared to a SURF features based algorithm in equivalent computation time. When compared to the TSSF based classifier, the HMT model has slightly degraded performance but almost an order of magnitude improvement in computation time enabling real time implementation. A second goal of this research is to classify the activity of objects identified as human. If the linear pyroelectric array sensor identifies the object as human, this then triggers a thermal video camera to capture video. From the video then, the goal is to recognize the activity as either suspicious or not based on a stored activity database. Recognition of human activity is crucial for surveillance and monitoring systems. In this thesis, we investigate the recognition of motion based activity in thermal infrared video. The segmentation of human poses or motions from known or unknown backgrounds is always a challenging task due to the lighting conditions and the colors of clothing and surfaces. ViBe: A universal background segmentation technique has been employed to improve the pose segmentation from the background. We have proposed a contrast based spatio-temporal template named temporal contrast image (TCI) which can capture small motion and is useful for repetitive and non-repetitive activity recognition. Hu's moment invariant feature descriptor and Naive Bayesian classifier are used for activity recognition. We have also combined our approach with existing spatio-temporal image formation techniques such as the gait energy image (GEI), motion energy image (MEI) and motion history image (MHI) for performance comparison. Experimental results on a limited set of activities demonstrate the effectiveness of our proposed approach. The method proposed in this work outperforms the statistical method for non-repetitive activity recognition. The overall goal of this research is to create a simple surveillance system that has real time moving object detection together with classification and human activity recognition. The profiling sensors described in this dissertation are relatively simple devices when compared to typical imaging cameras.

Download or read book Ambient Assisted Living written by Alessandro Leone and published by Springer. This book was released on 2019-02-02 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book documents the state of the art in the field of ambient assisted living (AAL), highlighting the impressive potential of novel methodologies and technologies to enhance well-being and promote active ageing. The coverage is wide ranging, with sections on care models and algorithms, enabling technologies and assistive solutions, elderly people monitoring, home rehabilitation, ICT solutions for AAL, living with chronic conditions, robotic assistance for the elderly, sensing technologies for AAL, and smart housing. The book comprises a selection of the best papers presented at the 9th Italian Forum on Ambient Assisted Living (ForitAAL 2018), which was held in Lecce, Italy, in July 2018 and brought together end users, technology teams, and policy makers to develop a consensus on how to improve provision for elderly and impaired people. Readers will find that the expert contributions offer clear insights into the ways in which the most recent exciting advances may be expected to assist in addressing the needs of the elderly and those with chronic conditions.

Download or read book Template Matching Techniques in Computer Vision written by Roberto Brunelli and published by John Wiley & Sons. This book was released on 2009-04-29 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: The detection and recognition of objects in images is a key research topic in the computer vision community. Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems. This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications. Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms. Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection. It is also of interest to graduate students undertaking studies in these areas.