Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network
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.
human action recognition compressed domain quantization parameters motion vectors pbl-mcrbfn meta-cognitive learning