Download or read book Pedestrian Detection Algorithms using Shearlets written by Lienhard Pfeifer and published by Logos Verlag Berlin GmbH. This book was released on 2019-01-15 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, we investigate the applicability of the shearlet transform for the task of pedestrian detection. Due to the usage of in several emerging technologies, such as automated or autonomous vehicles, pedestrian detection has evolved into a key topic of research in the last decade. In this time period, a wealth of different algorithms has been developed. According to the current results on pedestrian detection benchmarks, the algorithms can be divided into two categories. First, application of hand-crafted image features and of a classifier trained on these features. Second, methods using Convolutional Neural Networks in which features are learned during the training phase. It is studied how both of these types of procedures can be further improved by the incorporation of shearlets, a framework for image analysis which has a comprehensive theoretical basis. To this end, we adapt the shearlet framework according to the requirements of the practical application of pedestrian detection algorithms. One main application area of pedestrian detection is located in the automotive domain. In this field, algorithms have to be runable on embedded devices. Therefore, we study the embedded realization of a pedestrian detection algorithm based on the shearlet transform.
Download or read book Empirical Study of Pedestrian Detection Using Deep Learning written by Ahmet Kapkic and published by . This book was released on 2021 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: Detecting pedestrians in public settings is a major research topic in both Computer Vision and Artificial Intelligence communities. It has found applications in a wide range of areas such as vehicle driving with autonomous control systems, video surveillance, and navigating robots, etc. Over the past decade, a great progress has been made in the development of efficient algorithms and the availability of large-scale data set, especially the advancement of Deep Learning method. In this thesis, the performance of a few state-of-the-art methods were evaluated by conducting empirical experiments with different settings and dataset configurations on pedestrian detection. The experiments were carried out using several Deep Learning models in the framework of both baseline and special configurations, including the Faster R-CNN, Mask R-CNN, and Cascade R-CNN methods. The experimental results show that the Mask R-CNN with a ResNet50 barebone yields the best performance in terms of its larger AP improvement and fewer resource requirement. This work provides a solid foundation upon which more sophisticated comparative studies can be conducted that utilize new algorithms/models and larger data set.
Download or read book Vision based Pedestrian Detection and Estimation with a Blind Corner Camera written by Bastian Hartmann and published by GRIN Verlag. This book was released on 2011-08-12 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research Paper (undergraduate) from the year 2006 in the subject Electrotechnology, grade: 1,0, University Karlsruhe (TH), language: English, abstract: Avoiding collision accidents is becoming more and more an important topic in the research field of driver assistant systems. Especially for vision-based detection systems there are various approaches, which are built upon many different methods. This thesis deals with the avoidance of pedestrian accidents, caused by Blind Corner view problems. The presented approach comprises a pedestrian detection subsystem, which is part of a large camera system framework covering observation of the car environment. Based on a Blind Corner Camera and a neural network classification method, research in this thesis is focused on two aspects: detection improvement and danger level estimation. Since vision-based classification methods usually are still not able to yield perfect results, because of the complexity of this task, the detection result has to be improved by preprocessing and post processing. In this work, first, effects of image enhancement methods on detection are tested as preprocessing methods and, secondly, a new approach for a simple tracking and estimation strategy is presented, which improves detection in the way of a post processing method. Finally, information from tracking and prediction is used to estimate a danger level for pedestrians, which provides information about how collisionprone the current situations is.
Download or read book Pedestrian Detection in 3D Point Clouds Using Deep Neural Networks written by Oscar Lorente Corominas and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Pedestrian detection algorithms have always revolved around RGB scene information, but relying solely on it can be dangerous in situations where conventional cameras don't capture reality properly. For this reason, in recent years, many researchers have studied other alternatives that complement these existing techniques, such as the use of ultrasonic sensors or radars, that provide more reliable information in those situations. Another approach is to use LIDAR sensors, which map reality into point clouds using pulses of light. However, there are few studies that propose pedestrian detection techniques using only the data provided by a LIDAR. In this thesis, we explore this approach through the design and implementation of a pedestrian detection system in 3D point clouds. To do so, we train the PointNet++ point cloud classification network in order to demonstrate that the 3D geometric information of a scene is essential for the neural network to learn properly. Specifically, to carry out supervised training we need to generate a pedestrian and non-pedestrian ground truth in point clouds, so we have designed a semi-automatic labeling system based on the detection in RGB images and the subsequent transfer of these labels to the 3D domain. As a result, we train PointNet++ and test its performance on an outdoor dataset, obtaining outstanding results of up to 99.4% of accuracy and 98.6% of recall. With these results we are firmly corroborating the hypothesis stated in the thesis that 3D geometric information is essential for a neural network to learn to detect pedestrians in outdoor scenes. Not only that, we also surpass the results provided by a conventional detector in RGB images: YOLO, which provides a 48% of recall in the same dataset, thus proving that geometric information should not be an alternative in these systems, but a must.
Download or read book Pedestrian Detection and Tracking Using Stereo Vision Techniques written by Philip Kelly and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Improved Candidate Generation for Pedestrian Detection Using Background Modeling in Connected Vehicles written by and published by . This book was released on 2018 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: There has been a significant increase in road accidents in the past few years due to vehicle driving popularization around the world. More than half of road fatalities are attributed to pedestrians. In order to reduce the number of pedestrian fatalities, many safety features have been developed, such as Advanced Driver Assistance Systems (ADAS). In ADAS technology, many on-vehicle sensors are used to detect the surrounding of the vehilce, and then this information is used to prevent accidents by sending warnings to the driver or taking over control of the vehicle, such as applying a brake. Vision-based detection algorithms are a widely used technology in ADAS for pedestrian detection due to the rich information they provide and their low cost compared to other sensors. Vision-based pedestrian detection is done in the following steps: image acquisition, candidate generation, feature extraction, classification, and real-time object tracking. This work focused on advancing the candidate generation step of the process. Generating potential pedestrian candidates from the input image is an important step in the detection system, and it has a significant impact in the detection accuracy and the algorithm run-time. Classifying a large number of unnecessary candidates increases the processing requirements and may result in false positives. There are many approaches for candidate generation. The basic way is the sliding window approach, where the whole image is scanned by a sliding window at multiple scales of its original size. Other approaches are selective, and they focus on certain regions of interest in the image for candidate generation. The stereo-vision approach for candidate generation is an example of a selective approach, where a 3-D map is constructed for the image view, and then candidates are generated from certain regions based on depth values. The common disadvantage in the current candidate generation approaches is the generation of a large number of unnecessary candidates, many of which are static background objects. Also, some of these approaches are computationally expensive. This dissertation introduces a new approach for pedestrian detection in a road infrastructure environment. The main idea of the proposed approach is to utilize the image frames provided by the previous vehicles that passed by a certain road section to more intelligently generate candidates. Vehicle-to-Infrastructure (V2I) communication is used to transmit image frames collected by vehicles for a certain location to the infrastructure database. The images are processed in the infrastructure for background modeling and moving object extraction. Candidates are generated from the moving object regions in the processed image. The proposed approach eliminates the candidates generated from static background objects, such as trees and buildings. The proposed model improves the detection accuracy by reducing the false positives and reducing the run-time of the detection algorithms. The system architecture of the proposed model is provided. The infrastructure algorithms for background modeling and pedestrian detection are implemented, and the results are analyzed and compared to an industry standard reference algorithm.
Download or read book Ensemble Methods for Pedestrian Detection in Dense Crowds written by Jennifer Vandoni and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study deals with pedestrian detection in high- density crowds from a mono-camera system. The detections can be then used both to obtain robust density estimation, and to initialize a tracking algorithm. One of the most difficult challenges is that usual pedestrian detection methodologies do not scale well to high-density crowds, for reasons such as absence of background, high visual homogeneity, small size of the objects, and heavy occlusions. We cast the detection problem as a Multiple Classifier System (MCS), composed by two different ensembles of classifiers, the first one based on SVM (SVM-ensemble) and the second one based on CNN (CNN-ensemble), combined relying on the Belief Function Theory (BFT) to exploit their strengths for pixel-wise classification. SVM-ensemble is composed by several SVM detectors based on different gradient, texture and orientation descriptors, able to tackle the problem from different perspectives. BFT allows us to take into account the imprecision in addition to the uncertainty value provided by each classifier, which we consider coming from possible errors in the calibration procedure and from pixel neighbor's heterogeneity in the image space. However, scarcity of labeled data for specific dense crowd contexts reflects in the impossibility to obtain robust training and validation sets. By exploiting belief functions directly derived from the classifiers' combination, we propose an evidential Query-by-Committee (QBC) active learning algorithm to automatically select the most informative training samples. On the other side, we explore deep learning techniques by casting the problem as a segmentation task with soft labels, with a fully convolutional network designed to recover small objects thanks to a tailored use of dilated convolutions. In order to obtain a pixel-wise measure of reliability about the network's predictions, we create a CNN- ensemble by means of dropout at inference time, and we combine the different obtained realizations in the context of BFT. Finally, we show that the output map given by the MCS can be employed to perform people counting. We propose an evaluation method that can be applied at every scale, providing also uncertainty bounds on the estimated density.
Download or read book An Efficient Vision Based Pedestrian Detection and Tracking System for ITS Applications written by Tianyu Zuo and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis, a novel Pedestrian Protection System (PPS), composed of the Pedestrian Detection System (PDS) and the Pedestrian Tracking System (PTS), was proposed. The PPS is a supplementary application for the Advanced Driver Assistance System, which is used to avoid collisions between vehicles and pedestrians. The Pedestrian Detection System (PDS) is used to detect pedestrians from near to far ranges with the feature-classi er-based detection method (HOG + SVM). To achieve pedestrian detection from near to far ranges, a novel structure was proposed. The structure of our PDS consists of two cameras (called CS and CL separately). The CS is equipped with a short focal length lens to detect pedestrians in near-to-mid range; and, the CL is equipped with a long focal length lens to detect pedestrians in mid-to-far range. To accelerate the processing speed of pedestrian detection, the parallel computing capacity of GPU was utilized in the PDS. The synchronization algorithm is also introduced to synchronize the detection results of CS and CL. Based on the novel pedestrian detection structure, the detection process can reach a distance which is more than 130 meters away without decreasing detection accuracy. The detection range can be extended more than 100 meters without decreasing the processing speed of pedestrian detection. Afterwards, an algorithm to eliminate duplicate detection results is proposed to improve the detection accuracy. The Pedestrian Tracking System (PTS) is applied following the Pedestrian Detection System. The PTS is used to track the movement trajectory of pedestrians and to predict the future motion and movement direction. A C + + class (called pedestrianTracking class, which is short for PTC) was generated to operate the tracking process for every detected pedestrian. The Kalman lter is the main algorithm inside the PTC. During the operation of PPS, the nal detection results of each frame from PDS will be transmitted to the PTS to enable the tracking process. The new detection results will be used to update the existing tracking results in the PTS. Moreover, if there is a newly detected pedestrian, a new process will be generated to track the pedestrian in the PTS. Based on the tracking results in PTS, the movement trajectory of pedestrians can be obtained and their future motion and movement direction can be predicted. Two kinds of alerts are generated based on the predictions: warning alert and dangerous alert. These two alerts represent di erent situations; and, they will alert drivers to the upcoming situations. Based on the predictions and alerts, the collisions can be prevented e ectively. The safety of pedestrians can be guaranteed.
Download or read book Binary Matrix for Pedestrian Tracking in Infrared Images written by Keshava Grama and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary goal of this thesis is to present a robust low compute cost pedestrian tracking system for use with thermal infra-red images. Pedestrian tracking employs two distinct image analysis tasks, pedestrian detection and path tracking. This thesis will focus on benchmarking existing pedestrian tracking systems and using this to evaluate the proposed pedestrian detection and path tracking algorithm. The first part of the thesis describes the imaging system and the image dataset collected for evaluating pedestrian detection and tracking algorithms. The texture content of the images from the imaging system are evaluated using fourier maps following this the locations at which the dataset was collected are described. The second part of the thesis focuses on the detection and tracking system. To evaluate the performance of the tracking system, a time per target metric is described and is shown to work with existing tracking systems. A new pedestrian aspect ratio based pedestrian detection algorithm is proposed based on a binary matrix dynamically constrained using potential target edges. Results show that the proposed algorithm is effective at detecting pedestrians in infrared images while being less resource intensive as existing algorithms. The tracking system proposed uses deformable, dynamically updated codebook templates to track pedestrians in an infrared image sequence. Results show that this tracker performs as well as existing tracking systems in terms of accuracy, but requires fewer resources.
Download or read book Deep Learning and Parallel Computing Environment for Bioengineering Systems written by Arun Kumar Sangaiah and published by Academic Press. This book was released on 2019-07-26 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data
Download or read book Nature of Computation and Communication written by Phan Cong Vinh and published by Springer. This book was released on 2016-10-25 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the post-conference proceedings of the Second International Conference on Nature of Computation and Communication, ICTCC 2016, held in March 2016 in Rach Gia, Vietnam. The 36 revised full papers presented were carefully reviewed and selected from over 100 submissions. The papers cover formal methods for self-adaptive systems and discuss natural approaches and techniques for computation and communication.
Download or read book Fourth Congress on Intelligent Systems written by Sandeep Kumar and published by Springer Nature. This book was released on with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Image and Graphics written by Yao Zhao and published by Springer. This book was released on 2017-12-29 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNCS 10666, 10667, and 10668 constitutes the refereed conference proceedings of the 9thInternational Conference on Image and Graphics, ICIG 2017, held in Shanghai, China, in September 2017. The 172 full papers were selected from 370 submissions and focus on advances of theory, techniques and algorithms as well as innovative technologies of image, video and graphics processing and fostering innovation, entrepreneurship, and networking.
Download or read book Web and Big Data APWeb WAIM 2022 International Workshops written by Shiyu Yang and published by Springer Nature. This book was released on 2023-03-29 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes revised selected papers from the workshops of the 6th Asia-Pacific Web and Web-Age Information Management International Joint Conference on Web and Big Data, APWeb-WAIM 2022: The Fifth International Workshop on Knowledge Graph Management and Applications, KGMA 2022, The Fourth International Workshop on Semi-structured Big Data Management and Applications, SemiBDMA 2022, and The Third International Workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2022, held in Nanjing, China, in August 2022. The 23 papers were thoroughly reviewed and selected from the 39 submissions and present recent research on the theory, design, and implementation of data management systems.
Download or read book Image Fusion written by Gang Xiao and published by Springer Nature. This book was released on 2020-08-31 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically discusses the basic concepts, theories, research and latest trends in image fusion. It focuses on three image fusion categories – pixel, feature and decision – presenting various applications, such as medical imaging, remote sensing, night vision, robotics and autonomous vehicles. Further, it introduces readers to a new category: edge-preserving-based image fusion, and provides an overview of image fusion based on machine learning and deep learning. As such, it is a valuable resource for graduate students and scientists in the field of digital image processing and information fusion.
Download or read book Four Short Courses on Harmonic Analysis written by Brigitte Forster and published by Springer Science & Business Media. This book was released on 2010 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by internationally renowned mathematicians, this state-of-the-art textbook examines four research directions in harmonic analysis and features some of the latest applications in the field. The work is the first one that combines spline theory, wavelets, frames, and time-frequency methods leading up to a construction of wavelets on manifolds other than Rn. Four Short Courses on Harmonic Analysis is intended as a graduate-level textbook for courses or seminars on harmonic analysis and its applications. The work is also an excellent reference or self-study guide for researchers and practitioners with diverse mathematical backgrounds working in different fields such as pure and applied mathematics, image and signal processing engineering, mathematical physics, and communication theory.
Download or read book Local Binary Patterns New Variants and Applications written by Sheryl Brahnam and published by Springer. This book was released on 2013-09-01 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces Local Binary Patterns (LBP), arguably one of the most powerful texture descriptors, and LBP variants. This volume provides the latest reviews of the literature and a presentation of some of the best LBP variants by researchers at the forefront of textual analysis research and research on LBP descriptors and variants. The value of LBP variants is illustrated with reported experiments using many databases representing a diversity of computer vision applications in medicine, biometrics, and other areas. There is also a chapter that provides an excellent theoretical foundation for texture analysis and LBP in particular. A special section focuses on LBP and LBP variants in the area of face recognition, including thermal face recognition. This book will be of value to anyone already in the field as well as to those interested in learning more about this powerful family of texture descriptors.
