Action recognition by silhouette fusion
Pages 127 - 131
Abstract
Vision based human activity or action recognition is an important application of video surveillance, human computer interaction, logistic support, location based services and many more. In this paper, an intelligent system is proposed that recognizes human actions from videos and classifies them using the technique of silhouette frame fusion. For a given video sequence, background is subtracted from each frame and human silhouette is obtained. All the silhouettes from the extracted frames are fused and Local Phase Quantization (LPQ) features are extracted from them. SVM proves to be the best classifier to solve this multiclass problem, categorizing each defined activity, by giving 91.6 % accuracy. This two fold contribution towards the action recognition techniques is unique and simple yet powerful for identifying activities from a video without compromising the computational time. This novel and robust system works even if complete silhouette is not extracted- thus making it a practical approach that can be used in real time activity recognition.
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Index Terms
- Action recognition by silhouette fusion
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February 2017
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Published: 26 February 2017
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ICSCA 2017
ICSCA 2017: 2017 6th International Conference on Software and Computer Applications
February 26 - 28, 2017
Bangkok, Thailand
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