Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network - Venkatesh Babu, R.; Rangarajan, Badrinarayanan; Sundaram, Suresh; Tom, Manu (Applied soft computing, 2015)
•Proposed a fast human action recognition system in H.264 compressed domain.•Readily available quantization parameters and motion vectors are used for feature extraction.•The functional relationship between the features and action labels are classified using PBL-McRBFN which has a cognitive and meta-cognitive component.•Results are analyzed for various GOP parameters.•Achieved more than 90% accuracy using PBL-McRBFN on two benchmark datasets KTH and Weizmann. In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature.