Human interaction recognition through deep learning network
S. Jeba Berlin, Mala John · 2016
This paper provides an efficient framework for recognizing human interactions based on deep learning based architecture. The Harris corner points and the histogram form the feature vector of the spatiotemporal volume. The feature vector extraction is restricted to the region of interaction. A stacked autoencoder configuration is embedded in the deep learning framework used for classification. The method is evaluated on the benchmark UT interaction dataset and average recognition rates as high as 95% and 88% are obtained on setl and set2 respectively.