15,474 research outputs found

    Visual object tracking

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    University of Technology Sydney. Faculty of Engineering and Information Technology.Visual object tracking is a critical task in many computer-vision-related applications, such as surveillance and robotics. If the tracking target is provided in the first frame of a video, the tracker will predict the location and the shape of the target in the following frames. Despite the significant research effort that has been dedicated to this area for several years, this field remains challenging due to a number of issues, such as occlusion, shape variation and drifting, all of which adversely affect the performance of a tracking algorithm. This research focuses on incorporating the spatial and temporal context to tackle the challenging issues related to developing robust trackers. The spatial context is what surrounds a given object and the temporal context is what has been observed in the recent past at the same location. In particular, by considering the relationship between the target and its surroundings, the spatial context information helps the tracker to better distinguish the target from the background, especially when it suffers from scale change, shape variation, occlusion, and background clutter. Meanwhile, the temporal contextual cues are beneficial for building a stable appearance representation for the target, which enables the tracker to be robust against occlusion and drifting. In this regard, we attempt to develop effective methods that take advantage of the spatial and temporal context to improve the tracking algorithms. Our proposed methods can benefit three kinds of mainstream tracking frameworks, namely the template-based generative tracking framework, the pixel-wise tracking framework and the tracking-by-detection framework. For the template-based generative tracking framework, a novel template based tracker is proposed that enhances the existing appearance model of the target by introducing mask templates. In particular, mask templates store the temporal context represented by the frame difference in various time scales, and other templates encode the spatial context. Then, using pixel-wise analytic tools which provide richer details, which naturally accommodates tracking tasks, a finer and more accurate tracker is proposed. It makes use of two convolutional neural networks to capture both the spatial and temporal context. Lastly, for a visual tracker with a tracking-by-detection strategy, we propose an effective and efficient module that can improve the quality of the candidate windows sampled to identify the target. By utilizing the context around the object, our proposed module is able to refine the location and dimension of each candidate window, thus helping the tracker better focus on the target object

    A novel object tracking algorithm based on compressed sensing and entropy of information

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    Acknowledgments This research is supported by (1) the Ph.D. Programs Foundation of Ministry of Education of China under Grant no. 20120061110045, (2) the Science and Technology Development Projects of Jilin Province of China under Grant no. 20150204007G X, and (3) the Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China.Peer reviewedPublisher PD

    Review of Person Re-identification Techniques

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    Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201
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