Download or read book Spatio temporal Human Action Detection and Instance Segmentation in Videos written by Suman Saha and published by . This book was released on 2018 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Human Action Detection Tracking and Segmentation in Videos written by Yicong Tian and published by . This book was released on 2018 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation addresses the problem of human action detection, human tracking and segmentation in videos. They are fundamental tasks in computer vision and are extremely challenging to solve in realistic videos. We first propose a novel approach for action detection by exploring the generalization of deformable part models from 2D images to 3D spatiotemporal volumes. By focusing on the most distinctive parts of each action, our models adapt to intra-class variation and show robustness to clutter. This approach deals with detecting action performed by a single person. When there are multiple humans in the scene, humans need to be segmented and tracked from frame to frame before action recognition can be performed. Next, we propose a novel approach for multiple object tracking (MOT) by formulating detection and data association in one framework. Our method allows us to overcome the confinements of data association based MOT approaches, where the performance is dependent on the object detection results provided at input level. We show that automatically detecting and tracking targets in a single framework can help resolve the ambiguities due to frequent occlusion and heavy articulation of targets. In this tracker, targets are represented by bounding boxes, which is a coarse representation. However, pixel-wise object segmentation provides fine level information, which is desirable for later tasks. Finally, we propose a tracker that simultaneously solves three main problems: detection, data association and segmentation. This is especially important because the output of each of those three problems are highly correlated and the solution of one can greatly help improve the others. The proposed approach achieves more accurate segmentation results and also helps better resolve typical difficulties in multiple target tracking, such as occlusion, ID-switch and track drifting.
Download or read book Modelling Human Motion written by Nicoletta Noceti and published by Springer Nature. This book was released on 2020-07-09 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new frontiers of robotics research foresee future scenarios where artificial agents will leave the laboratory to progressively take part in the activities of our daily life. This will require robots to have very sophisticated perceptual and action skills in many intelligence-demanding applications, with particular reference to the ability to seamlessly interact with humans. It will be crucial for the next generation of robots to understand their human partners and at the same time to be intuitively understood by them. In this context, a deep understanding of human motion is essential for robotics applications, where the ability to detect, represent and recognize human dynamics and the capability for generating appropriate movements in response sets the scene for higher-level tasks. This book provides a comprehensive overview of this challenging research field, closing the loop between perception and action, and between human-studies and robotics. The book is organized in three main parts. The first part focuses on human motion perception, with contributions analyzing the neural substrates of human action understanding, how perception is influenced by motor control, and how it develops over time and is exploited in social contexts. The second part considers motion perception from the computational perspective, providing perspectives on cutting-edge solutions available from the Computer Vision and Machine Learning research fields, addressing higher-level perceptual tasks. Finally, the third part takes into account the implications for robotics, with chapters on how motor control is achieved in the latest generation of artificial agents and how such technologies have been exploited to favor human-robot interaction. This book considers the complete human-robot cycle, from an examination of how humans perceive motion and act in the world, to models for motion perception and control in artificial agents. In this respect, the book will provide insights into the perception and action loop in humans and machines, joining together aspects that are often addressed in independent investigations. As a consequence, this book positions itself in a field at the intersection of such different disciplines as Robotics, Neuroscience, Cognitive Science, Psychology, Computer Vision, and Machine Learning. By bridging these different research domains, the book offers a common reference point for researchers interested in human motion for different applications and from different standpoints, spanning Neuroscience, Human Motor Control, Robotics, Human-Robot Interaction, Computer Vision and Machine Learning. Chapter 'The Importance of the Affective Component of Movement in Action Understanding' of this book is available open access under a CC BY 4.0 license at link.springer.com.
Download or read book Video Representation for Fine grained Action Recognition written by Yang Zhou and published by . This book was released on 2016 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, fine-grained action analysis has raised a lot of research interests due to its potential applications in smart home, medical surveillance, daily living assist and child/elderly care, where action videos are captured indoor with fixed camera. Although background motion (i.e. one of main challenges for general action recognition) is more controlled compared to general action recognition, it is widely acknowledged that fine-grained action recognition is very challenging due to large intra-class variability, small inter-class variability, large variety of action categories, complex motions and complicated interactions. Fine-Grained actions, especially the manipulation sequences involve a large amount of interactions between hands and objects, therefore how to model the interactions between human hands and objects (i.e., context) plays an important role in action representation and recognition. We propose to discover the manipulated objects by human by modeling which objects are being manipulated and how they are being operated. Firstly, we propose a representation and classification pipeline which seamlessly incorporates localized semantic information into every processing step for fine-grained action recognition. In the feature extraction stage, we explore the geometric information between local motion features and the surrounding objects. In the feature encoding stage, we develop a semantic-grouped locality-constrained linear coding (SG-LLC) method that captures the joint distributions between motion and object-in-use information. Finally, we propose a semantic-aware multiple kernel learning framework (SA-MKL) by utilizing the empirical joint distribution between action and object type for more discriminative action classification. This approach can discover and model the inter- actions between human and objects. However, discovering the detailed knowledge of pre-detected objects (e.g. drawer and refrigerator). Thus, the performance of action recognition is constrained by object recognition, not to mention detection of objects requires tedious human labor for object annotation. Secondly, we propose a mid-level video representation to be suitable for fine-grained action classification. Given an input video sequence, we densely sample a large amount of spatio-temporal motion parts by temporal segmentation with spatial segmentation, and represent them with local motion features. The dense mid-level candidate parts are rich in localized motion information, which is crucial to fine-grained action recognition. From the candidate spatio-temporal parts, we perform an unsupervised approach to discover and learn the representative part detectors for final video representation. By utilizing the dense spatio-temporal motion parts, we highlight the human-object interactions and localized delicate motion in the local spatio-temporal sub-volume of the video. Thirdly, we propose a novel fine-grained action recognition pipeline by interaction part proposal and discriminative mid-level part mining. Firstly, we generate a large number of candidate object regions using off-the-shelf object proposal tool, e.g., BING. Secondly, these object regions are matched and tracked across frames to form a large spatio-temporal graph based on the appearance matching and the dense motion trajectories through them. We then propose an efficient approximate graph segmentation algorithm to partition and filter the graph into consistent local dense sub-graphs. These sub-graphs, which are spatio-temporal sub-volumes, represent our candidate interaction parts. Finally, we mine discriminative mid-level part detectors from the features computed over the candidate interaction parts. Bag-of-detection scores based on a novel Max-N pooling scheme are computed as the action representation for a video sample. Finally, we also focus on the first-view (egocentric) action recognition problem, which contains lots of hand-object interactions. On one hand, we propose a novel end-to-end trainable semantic parsing network for hand segmentation. On the other hand, we propose a second end-to-end deep convolutional network to maximally utilize the contextual information among hand, foreground object, and motion for interactional foreground object detection.
Download or read book Video Object Segmentation written by Ning Xu and published by Springer Nature. This book was released on with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Spatiotemporal Representation Learning For Human Action Recognition And Localization written by Alaaeldin Ali and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Human action understanding from videos is one of the foremost challenges in computer vision. It is the cornerstone of many applications like human-computer interaction and automatic surveillance. The current state of the art methods for action recognition and localization mostly rely on Deep Learning. In spite of their strong performance, Deep Learning approaches require a huge amount of labeled training data. Furthermore, standard action recognition pipelines rely on independent optical flow estimators which increase their computational cost. We propose two approaches to improve these aspects. First, we develop a novel method for efficient, real-time action localization in videos that achieves performance on par or better than other more computationally expensive methods. Second, we present a self-supervised learning approach for spatiotemporal feature learning that does not require any annotations. We demonstrate that features learned by our method provide a very strong prior for the downstream task of action recognition.
Download or read book Action Recognition Temporal Localization and Detection in Trimmed and Untrimmed Videos written by Rui Hou and published by . This book was released on 2019 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Automatic understanding of videos is one of the most active areas of computer vision research. It has applications in video surveillance, human computer interaction, video sports analysis, virtual and augmented reality, video retrieval etc. In this dissertation, we address four important tasks in video understanding, namely action recognition, temporal action localization, spatial-temporal action detection and video object/action segmentation. This dissertation makes contributions to above tasks by proposing. First, for video action recognition, we propose a category level feature learning method. Our proposed method automatically identifies such pairs of categories using a criterion of mutual pairwise proximity in the (kernelized) feature space, and a category-level similarity matrix where each entry corresponds to the one-vs-one SVM margin for pairs of categories. Second, for temporal action localization, we propose to exploit the temporal structure of actions by modeling an action as a sequence of sub-actions and present a computationally efficient approach. Third, we propose 3D Tube Convolutional Neural Network (TCNN) based pipeline for action detection. The proposed architecture is a unified deep network that is able to recognize and localize action based on 3D convolution features. It generalizes the popular faster R-CNN framework from images to videos. Last, an end-to-end encoder-decoder based 3D convolutional neural network pipeline is proposed, which is able to segment out the foreground objects from the background. Moreover, the action label can be obtained as well by passing the foreground object into an action classifier. Extensive experiments on several video datasets demonstrate the superior performance of the proposed approach for video understanding compared to the state-of-the-art.
Download or read book Human Action Localization and Recognition in Unconstrained Videos written by Hakan Boyraz and published by . This book was released on 2013 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: As imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing discriminative sub-regions of images and videos when performing recognition tasks. In this thesis, we address the action detection and recognition problems. Action detection in video is a particularly difficult problem because actions must not only be recognized correctly, but must also be localized in the 3D spatio-temporal volume. We introduce a technique that transforms the 3D localization problem into a series of 2D detection tasks. This is accomplished by dividing the video into overlapping segments, then representing each segment with a 2D video projection. The advantage of the 2D projection is that it makes it convenient to apply the best techniques from object detection to the action detection problem. We also introduce a novel, straightforward method for searching the 2D projections to localize actions, termed Two- Point Subwindow Search (TPSS). Finally, we show how to connect the local detections in time using a chaining algorithm to identify the entire extent of the action. Our experiments show that video projection outperforms the latest results on action detection in a direct comparison.
Download or read book Spatio temporal Volume based Video Event Detection written by Jing Wang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Online and offline video clips provide rich information on dynamic events that occurred over a period of time, for example, human actions, crowd behaviours, and other subject pattern changes. Although substantial progresses have been made in the last 3 decades on 2D image feature processing and their applications in areas such as face matching and objects recognition, video event detection still remains one of the most challenging fields in computer vision study due to the wide range of continuous and non-linear signals engaged by an imaging system, and the inherent semantic difficulties in machine-based understanding of the detected feature patterns. For bridging the gap between the pixel-level image features and the semantic "meanings" of a videoed single human event, this research has investigated the problem domain through employing the 3D Spatio-Temporal Volume (STV) structure and its global feature paradigm for event pattern recognition. The process pipeline follows an improved Pair-wise Region Comparison (I-PWRC) and a region intersection (RI) based 3D template matching approach for detecting and identifying human actions under uncontrolled real-world videoing conditions. To maintain the run-time performance of this innovative system design, this programme has also developed an efficient pre-filtering mechanism to reduce the amount of voxels (volumetric pixels) that need to be processed in each operational cycle. For further improving the system's adaptability and robustness, several optimisation techniques, such as scale-invariant template matching and event location prediction mechanisms, have also been developed and implemented. The proposed design has been tested on various renowned online computer vision research databases and been benchmarked against other classic implementation strategies and systems. Satisfactory evaluation results have been obtained through statistical analyses on standard test criteria such as "Recall" rate and the processing efficiency.
Download or read book Spatio temporal Modeling for Action Recognition in Videos written by Guoxi Huang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Analysis of Human centric Activities in Video Via Qualitative Spatio temporal Reasoning written by Hajar Sadeghi Sokeh and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applying qualitative spatio-temporal reasoning in video analysis is now a very active research topic in computer vision and artificial intelligence. Among all video analysis applications, monitoring and understanding human activities is of great interest. Many human activities can be understood by analysing the interaction between objects in space and time. Qualitative spatio-temporal reasoning encapsulates information that is useful for analysing huma-centric videos. This information can be represented in a very compact form involving interactions between objects of interest in the form of qualitative spatio-temporal relationships. This thesis focuses on three different aspects of interpreting human-centric videos; first introducing a representation of interactions between objects of interest, second determining which objects in the scene are relevant to the activity, and third recognising of human actions by applying the proposed representation model between human body joints and body parts. As a first contribution, we present an accurate and comprehensive model for representing several aspects of space over time from videos called "AngledCORE-9", a modified version of CORE-9 (proposed by Cohn et al. [2012]). This model is as efficient as CORE-9 and allows us to extract spatial information with much higher accuracy than previously possible. We evaluate our new knowledge representation method on a real video dataset to perform action clustering. Our next contribution is proposing a model for differentiating relevant from irrelevant objects to the human actions in the videos. The chief issue of recognising different human actions in videos using spatio-temporal features is that there are usually many moving objects in the scene. No existing method can successfully find the involved objects in the activity. The output of our system is a list of tracks for all possible objects in the video with their probabilities for being involved in the activity. The track with the highest probability is most likely to be the object with which the person is interacting. Knowing the involved object(s) in the activities is very advantageous. Since it can be used to improve the human action recognition rate. Finally, instead of looking at human-object interactions, we consider skeleton joints as the points of interest. Working on joints provides more information about how a person is moving to perform the activity. In this part of the thesis, we use videos with human skeletons in 3D captured by Kinect, MSR3D-action dataset. We use our proposed model "AngledCORE-9" to extract features and describe the temporal variation of these features frame by frame. We compare our results against some of the recent works on the same dataset.
Download or read book Spatiotemporal Graphs for Object Segmentation and Human Pose Estimation in Videos written by Dong Zhang and published by . This book was released on 2016 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: Images and videos can be naturally represented by graphs, with spatial graphs for images and spatiotemporal graphs for videos. However, for different applications, there are usually different formulations of the graphs, and algorithms for each formulation have different complexities. Therefore, wisely formulating the problem to ensure an accurate and efficient solution is one of the core issues in Computer Vision research. We explore three problems in this domain to demonstrate how to formulate all of these problems in terms of spatiotemporal graphs and obtain good and efficient solutions. The first problem we explore is video object segmentation. The goal is to segment the primary moving objects in the videos. This problem is important for many applications, such as content based video retrieval, video summarization, activity understanding and targeted content replacement. In our framework, we use object proposals, which are object-like regions obtained by lowlevel visual cues. Each object proposal has an object-ness score associated with it, which indicates how likely this object proposal corresponds to an object. The problem is formulated as a directed acyclic graph, for which nodes represent the object proposals and edges represent the spatiotemporal relationship between nodes. A dynamic programming solution is employed to select one object proposal from each video frame, while ensuring their consistency throughout the video frames. Gaussian mixture models (GMMs) are used for modeling the background and foreground, and Markov Random Fields (MRFs) are employed to smooth the pixel-level segmentation.
Download or read book Scalable Action Recognition in Continuous Video Streams written by Hamed Pirsiavash and published by . This book was released on 2012 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Activity recognition in video has a variety of applications, including rehabilitation, surveillance, and video retrieval. It is relatively easy for a human to recognize actions in a video once he/she watches it. However, in many applications the videos are very long, eg. in life-logging, and/or we need the real-time detection, eg. in human computer interaction. This motivates us to build computer vision and artificial intelligence algorithms to recognize activities in video sequences automatically. We are addressing several challenges in activity recognition, including (1) computational scalability, (2) spatio-temporal feature extraction, (3) spatio-temporal models, and finally, (4) dataset development. (1) Computational Scalability: We develop ``steerable'' models that parsimoniously represent a large collection of templates with a small number of parameters. This results in local detectors scalable enough for a large number of frames and object/action categories. (2) Spatio-temporal feature extraction: Spatio-temporal feature extraction is difficult for scenes with many moving objects that interact and occlude each other. We tackle this problem using the framework of multi-object tracking and developing linear-time, scalable graph-theoretic algorithms for inference. (3) Spatio-temporal models: Actions exhibit complex temporal structure, such as sub-actions of variable durations and compositional orderings. Much research on action recognition ignores such structure and instead focuses on K-way classification of temporally pre-segmented video clips \cite{poppe2010survey, DBLP:journals/csur/AggarwalR11}. We describe lightweight and efficient grammars that segment a continuous video stream into a hierarchical parse of multiple actions and sub-actions. (4) Dataset development: Finally, in terms of evaluation, video benchmarks are relatively scarce compared to the abundance of image benchmarks. It appears difficult to collect (and annotate) large-scale, unscripted footage of people doing interesting things. We discuss one solution, introducing a new, large-scale benchmark for the problem of detecting activities of daily living (ADL) in first-person camera views.
Download or read book Comparative Evaluation of Local Spatio temporal Features for Human Action Recognition written by Divye Kumar and published by . This book was released on 2010 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Computer Vision ACCV 2014 written by Daniel Cremers and published by Springer. This book was released on 2015-04-16 with total page 699 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five-volume set LNCS 9003--9007 constitutes the thoroughly refereed post-conference proceedings of the 12th Asian Conference on Computer Vision, ACCV 2014, held in Singapore, Singapore, in November 2014. The total of 227 contributions presented in these volumes was carefully reviewed and selected from 814 submissions. The papers are organized in topical sections on recognition; 3D vision; low-level vision and features; segmentation; face and gesture, tracking; stereo, physics, video and events; and poster sessions 1-3.
Download or read book Computer Vision ACCV 2020 written by Hiroshi Ishikawa and published by Springer Nature. This book was released on 2021-02-25 with total page 718 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.
Download or read book Human Activity Recognition in Video written by Ross Messing and published by . This book was released on 2011 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This thesis explores the problem of recognizing complex human activities involving the manipulation of objects in high resolution video. Inspired by human psychophysical performance, I develop and evaluate an activity recognition feature derived from the velocity histories of tracked keypoints. These features have a much greater spatial and temporal range than existing video features. I show that a generative mixture model using these features performs comparably to local spatio-temporal features on the KTH activity recognition dataset. I additionally introduce and explore a new activity recognition dataset of activities of daily living (URADL), containing high resolution video sequences of complex activities. I demonstrate the superior performance of my velocity history feature on this dataset, and explore ways in which it can be extended. I investigate the value of a more sophisticated latent velocity model for velocity histories. I explore the addition of contextual semantic information to the model, whether fully automatic or derived from supervision, and provide a sketch for the inclusion of this information in any feature-based generative model for activity recognition or time series data. This approach performs comparably to established methods on the KTH dataset, and significantly outperforms local spatio-temporal features on the challenging new URADL dataset. I further develop another new dataset, URADL2, and explore transferring knowledge between related video activity recognition domains. Using a straightforward feature-expansion transfer learning technique, I show improved performance on one dataset using activity models transferred from the other dataset"--Leaves iv-v.
