Journal
MATHEMATICAL PROBLEMS IN ENGINEERING
Volume 2015, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2015/387464
Keywords
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Funding
- Research Foundation for Science & Technology Office of Hunan Province [2014FJ3057]
- Hunan Provincial Education Science and Twelve Five planning issues [XJK012CGD022]
- Teaching Reform Foundation of Hunan Province Ordinary College [2012401544]
- Foundation for Key Constructive Discipline of Hunan Province
- Foundation for Key Laboratory of Information Technology and Information Security in Hunan Province
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A novel method based on hybrid feature is proposed for human action recognition in video image sequences, which includes two stages of feature extraction and action recognition. Firstly, we use adaptive background subtraction algorithm to extract global silhouette feature and optical flow model to extract local optical flow feature. Then we combine global silhouette feature vector and local optical flow feature vector to form a hybrid feature vector. Secondly, in order to improve the recognition accuracy, we use an optimized Multiple Instance Learning algorithm to recognize human actions, in which an Iterative Querying Heuristic (IQH) optimization algorithm is used to train the Multiple Instance Learning model. We demonstrate that our hybrid feature-based action representation can effectively classify novel actions on two different data sets. Experiments show that our results are comparable to, and significantly better than, the results of two state-of-the-art approaches on these data sets, which meets the requirements of stable, reliable, high precision, and anti-interference ability and so forth.
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