Spatio Temporal Human Action Detection And Instance Segmentation In Videos
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Spatio Temporal Human Action Detection And Instance Segmentation In Videos
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Author : Suman Saha
language : en
Publisher:
Release Date : 2018
Spatio Temporal Human Action Detection And Instance Segmentation In Videos written by Suman Saha and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Human Action Detection Tracking And Segmentation In Videos
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Author : Yicong Tian
language : en
Publisher:
Release Date : 2018
Human Action Detection Tracking And Segmentation In Videos written by Yicong Tian and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
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.
Video Representation For Fine Grained Action Recognition
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Author : Yang Zhou
language : en
Publisher:
Release Date : 2016
Video Representation For Fine Grained Action Recognition written by Yang Zhou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with High definition video recording categories.
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.
Video Object Segmentation
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Author : Ning Xu
language : en
Publisher: Springer Nature
Release Date :
Video Object Segmentation written by Ning Xu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Modelling Human Motion
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Author : Nicoletta Noceti
language : en
Publisher: Springer Nature
Release Date : 2020-07-09
Modelling Human Motion written by Nicoletta Noceti and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-09 with Computers categories.
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.
Spatiotemporal Representation Learning For Human Action Recognition And Localization
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Author : Alaaeldin Ali
language : en
Publisher:
Release Date : 2019
Spatiotemporal Representation Learning For Human Action Recognition And Localization written by Alaaeldin Ali and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
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.
Human Action Localization And Recognition In Unconstrained Videos
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Author : Hakan Boyraz
language : en
Publisher:
Release Date : 2013
Human Action Localization And Recognition In Unconstrained Videos written by Hakan Boyraz and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
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.
Action Recognition Temporal Localization And Detection In Trimmed And Untrimmed Videos
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Author : Rui Hou
language : en
Publisher:
Release Date : 2019
Action Recognition Temporal Localization And Detection In Trimmed And Untrimmed Videos written by Rui Hou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
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.
Spatiotemporal Graphs For Object Segmentation And Human Pose Estimation In Videos
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Author : Dong Zhang
language : en
Publisher:
Release Date : 2016
Spatiotemporal Graphs For Object Segmentation And Human Pose Estimation In Videos written by Dong Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
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.
Scalable Action Recognition In Continuous Video Streams
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Author : Hamed Pirsiavash
language : en
Publisher:
Release Date : 2012
Scalable Action Recognition In Continuous Video Streams written by Hamed Pirsiavash and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
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.