Action recognition using Feature Position Constrained Linear Coding

Recently space-time interest points (STIPs) using bag-of-feature (BOF) in action recognition has been highly successful. Despite its popularity, The quantization error and the lost of semantic meaning among STIPs are the main weaknesses that severely limit the effectiveness of this method. To overcome these limitations, this paper incorporated the feature position information into coding procedure and proposed a novel Feature Position Constrained Linear Coding (FPLC) method by extending the Locality Constrained Linear Coding (LLC) approach. It first project the features into the human ROI, then codes the features locally using FPLC with the consideration of feature position. Owning to that the local area of human ROI often aggregate features extracted from the same part of human body and those features should exhibit similar values, this local coding strategy helps to alleviate the quantization error and enhance correlation between features at the same time, which helps to improve the recognition accuracy. Compared with the state-of-the-art action recognition method, experiment results demonstrated the effectiveness of the proposed method.