多視角環境下之行為分析與辨識

邱宜達

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多視角環境下之行為分析與辨識

Multi-View Behavior Analysis and Recognition

邱宜達 , Masters　　Advisor：謝君偉　　

英文

多角度；行為分析；； multi-view；behavior analysis；action recognition

Abstract │ Reference (34) │ Altmetrics

Abstract 〈TOP〉

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This paper presents a new behavior classification system that can be used to analyze human movements from any views and directly form videos.  Technically, if a person is observed from other views, his appearances will change significantly.  To freely recognize his behaviors, traditional methods tend to adopt 3-D data for behavior modeling and analysis.  However, its inherent correspondence process is very time-consuming and will make it inappropriate for real time applications.  To tackle this problem, this thesis proposes a novel human representation scheme for recognizing human behaviors from any views.  In this scheme, a novel view alignment method is first proposed for mapping each action sequence (captured from any views) to a fixed view.  To achieve this mapping, the spatial and temporal features should be first extracted from each action sequence.  To extract the spatial feature, the central contexts of each posture are then extracted through a triangulation technique.  Then, a set of key postures is selected and built for converting an action to a symbol string.  To reduce the converting errors, a transitional probably table is built for recording the possibility of one posture transferring to another posture.  With the table and the central context feature, each action sequence can be then aligned to a fixed view and then represented by an action matrix.  After that, matching two action sequences from arbitrary views will become a single-view matrix matching problem.  Then, the Viterbi algorithm is used for aligning two action sequences and then classifying them to different behavior types.  Experimental results prove that the proposed method is a robust and accurate tool for human movement analysis from any views.

Reference ( 34 ) 〈TOP〉

[1] T. B. Moeslund and E. Granum, “A survey of computer vision-based human motion capture,” Computer Vision and Image Understanding, vol. 81, no. 3, pp. 231-268, Mar. 2001.
連結：
[2] R. Cucchiara, C. Grana, A. Prati, and R. Vezzani, “Probabilities posture classification for human-behavior analysis,” IEEE Transactions on Systems, Man, and Cybernetics-Part A: System and Humans, vol. 35, no. 1: pp. 42-54, Jan. 2005.
連結：
[3] Haritaoglu, D. Harwood, and L.S. Davis, “W4: real-time surveillance of people and their activities,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, 2000.
連結：
[5] I. Mikic, et al., “Human body model acquisition and tracking using voxel data,” International Journal of Computer Vision, vol. 53, no. 3, pp. 199-223, 2003.
連結：
[6] N. Werghi, “A discriminative 3D wavelet-based descriptors: application to the recognition of human body postures,” Pattern Recognition Letter, vol. 26, no. 5, pp.663-677, 2005.
連結：

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