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Full-Text Articles in Physical Sciences and Mathematics
Exploring Sparsity, Self-Similarity, And Low Rank Approximation In Action Recognition, Motion Retrieval, And Action Spotting, Chuan Sun
Electronic Theses and Dissertations
This thesis consists of 4 major parts. In the first part (Chapters 1-2), we introduce the overview, motivation, and contribution of our works, and extensively survey the current literature for 6 related topics. In the second part (Chapters 3-7), we explore the concept of "Self-Similarity" in two challenging scenarios, namely, the Action Recognition and the Motion Retrieval. We build three-dimensional volume representations for both scenarios, and devise effective techniques that can produce compact representations encoding the internal dynamics of data. In the third part (Chapter 8), we explore the challenging action spotting problem, and propose a feature-independent unsupervised framework that …
Study Of Human Activity In Video Data With An Emphasis On View-Invariance, Nazim Ashraf
Study Of Human Activity In Video Data With An Emphasis On View-Invariance, Nazim Ashraf
Electronic Theses and Dissertations
The perception and understanding of human motion and action is an important area of research in computer vision that plays a crucial role in various applications such as surveillance, HCI, ergonomics, etc. In this thesis, we focus on the recognition of actions in the case of varying viewpoints and different and unknown camera intrinsic parameters. The challenges to be addressed include perspective distortions, differences in viewpoints, anthropometric variations, and the large degrees of freedom of articulated bodies. In addition, we are interested in methods that require little or no training. The current solutions to action recognition usually assume that there …
A Study Of Localization And Latency Reduction For Action Recognition, Syed Zain Masood
A Study Of Localization And Latency Reduction For Action Recognition, Syed Zain Masood
Electronic Theses and Dissertations
The success of recognizing periodic actions in single-person-simple-background datasets, such as Weizmann and KTH, has created a need for more complex datasets to push the performance of action recognition systems. In this work, we create a new synthetic action dataset and use it to highlight weaknesses in current recognition systems. Experiments show that introducing background complexity to action video sequences causes a significant degradation in recognition performance. Moreover, this degradation cannot be fixed by fine-tuning system parameters or by selecting better feature points. Instead, we show that the problem lies in the spatio-temporal cuboid volume extracted from the interest point …
Spatio-Temporal Maximum Average Correlation Height Templates In Action Recognition And Video Summarization, Mikel Rodriguez
Spatio-Temporal Maximum Average Correlation Height Templates In Action Recognition And Video Summarization, Mikel Rodriguez
Electronic Theses and Dissertations
Action recognition represents one of the most difficult problems in computer vision given that it embodies the combination of several uncertain attributes, such as the subtle variability associated with individual human behavior and the challenges that come with viewpoint variations, scale changes and different temporal extents. Nevertheless, action recognition solutions are critical in a great number of domains, such video surveillance, assisted living environments, video search, interfaces, and virtual reality. In this dissertation, we investigate template-based action recognition algorithms that can incorporate the information contained in a set of training examples, and we explore how these algorithms perform in action …
Learning Semantic Features For Visual Recognition, Jingen Liu
Learning Semantic Features For Visual Recognition, Jingen Liu
Electronic Theses and Dissertations
Visual recognition (e.g., object, scene and action recognition) is an active area of research in computer vision due to its increasing number of real-world applications such as video (image) indexing and search, intelligent surveillance, human-machine interaction, robot navigation, etc. Effective modeling of the objects, scenes and actions is critical for visual recognition. Recently, bag of visual words (BoVW) representation, in which the image patches or video cuboids are quantized into visual words (i.e., mid-level features) based on their appearance similarity using clustering, has been widely and successfully explored. The advantages of this representation are: no explicit detection of objects or …
Object Tracking And Activity Recognition In Video Acquired Using Mobile Cameras, Alper Yilmaz
Object Tracking And Activity Recognition In Video Acquired Using Mobile Cameras, Alper Yilmaz
Electronic Theses and Dissertations
Due to increasing demand on deployable surveillance systems in recent years, object tracking and activity recognition are receiving considerable attention in the research community. This thesis contributes to both the tracking and the activity recognition components of a surveillance system. In particular, for the tracking component, we propose two different approaches for tracking objects in video acquired by mobile cameras, each of which uses a different object shape representation. The first approach tracks the centroids of the objects in Forward Looking Infrared Imagery (FLIR) and is suitable for tracking objects that appear small in airborne video. The second approach tracks …