Gesture recognition using histograms of optical flow

In the field of Computer Vision, Gesture Recognition is kind of crucial problem. What differ video classification from normal image classification is that the enormous amount number of video data can not be ignored, because those complex data could lead to significant decline of computational efficiency, Therefore, this article mainly focus on how to obtain a video classification with both accuracy and efficiency in the meanwhile. In order to create a n ideal video classification system, the article use an improved speed-up Bag-of-Words model as basic pipeline. In each part of the pipeline, we apply and evaluate various strategies. In particular, in the step of feature extraction, we create a type of fast information feature descriptor for video, which is called Histogram of Optical Flow. Besides, we try to modify frame sampling rate of video, aiming to reduce calculation. In the process of creating features, we use sampling rate which is same to the size of a block. In this way, each block could be used repeatedly and the calculation will be reduced. When building visual word vocabulary and using SVM for classification, we use different methods to find a best performance of our system. As a final result, we get a trade-off between efficiency and accuracy of our gesture recognition system.