Compressed Domain Feature Analysis for Efficient Action Recognition
Video surveillance system that use video cameras to transmit a signal to a specific place, on a limited set of monitors by Closed circuit television (CCTV) that may need monitoring such as banks, casinos, airports, military installations, and convenience stores. Even though CCTV systems may monitor a particular event, it still requires recognized data are to be more accurate and appearance invariant when abnormal activities are occurred and analysed. Automatic Teller Machines (ATM) are widely used over the world for automatic banking transactions. Some of ATM users are facing with unlawful activities such as money snatching and customer assault by murder. CCTV devices are set up for security that becomes an important factor for covering criminal activities. Many researchers are trying to solve this issue in different ways of Human Activity Recognition. In our system, we tend to use compressed video sequences MPEG flow instead of computationally expensive Optical Flow (OF) Compressed domain features such as Motion Vectors (MV) and Discrete Cosine Transform (DCT) coefficients can be achieved by partial decoding that needs no prior segmentation and no spatial or temporal assignment therefore it can reduce time requirement and can effect in minimal training. Mapping of compressed domain features are effective for no variation in actions recognition because of noise reduction in motion contents. Bag Of Words model based descriptors are created for each group of features by motion segmentation getting accurate feature description. Our system tends to create reliable and real time action recognition with compressed domain features analysis.