|Incremental Learning for Video-Based Gait Recognition With LBP Flow|
|Maodi Hu; Yunhong Wang; Zhaoxiang Zhang; De Zhang; Little, James J
|Source Publication||IEEE TRANSACTIONS ON CYBERNETICS
|Abstract||Gait analysis provides a feasible approach for identification in intelligent video surveillance. However, the effectiveness of the dominant silhouette-based approaches is overly dependent upon background subtraction. In this paper, we propose a novel incremental framework based on optical flow, including dynamics learning, pattern retrieval, and recognition. It can greatly improve the usability of gait traits in video surveillance applications. Local binary pattern (LBP) is employed to describe the texture information of optical flow. This representation is called LBP flow, which performs well as a static representation of gait movement. Dynamics within and among gait stances becomes the key consideration for multiframe detection and tracking, which is quite different from existing approaches. To simulate the natural way of knowledge acquisition, an individual hidden Markov model (HMM) representing the gait dynamics of a single subject incrementally evolves from a population model that reflects the average motion process of human gait. It is beneficial for both tracking and recognition and makes the training process of the HMM more robust to noise. Extensive experiments on widely adopted databases have been carried out to show that our proposed approach achieves excellent performance.|
|Keyword||Local Binary Pattern (Lbp) Flow
Individual Hidden Markov Model (Hmm) (iHmm)
|Corresponding Author||Zhaoxiang Zhang|
Maodi Hu,Yunhong Wang,Zhaoxiang Zhang,et al. Incremental Learning for Video-Based Gait Recognition With LBP Flow[J]. IEEE TRANSACTIONS ON CYBERNETICS,2012,43(1):77-89.
Maodi Hu,Yunhong Wang,Zhaoxiang Zhang,De Zhang,&Little, James J.(2012).Incremental Learning for Video-Based Gait Recognition With LBP Flow.IEEE TRANSACTIONS ON CYBERNETICS,43(1),77-89.
Maodi Hu,et al."Incremental Learning for Video-Based Gait Recognition With LBP Flow".IEEE TRANSACTIONS ON CYBERNETICS 43.1(2012):77-89.
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