Slow feature analysis for multi-camera activity understanding

Multi-camera activity analysis is a key point in video surveillance of many wide-area scenes, such as airports, underground stations, shopping mall and road junctions. On the basis of previous work, this paper presents a new feature learning method based on Slow Feature Analysis (SFA) to understand activities observed across the network of cameras. The main contribution of this paper can be summarized as follows: (1) It is the first time that SFA-based learning method is introduced to multi-camera activity understanding; (2) It presents an evaluation to examine the effectiveness of SFA-based method to facilitate the learning of inter-camera activity pattern dependencies; and (3) It estimates the sensitivity of learning inter-camera time delayed dependency given different training size, which is a critical factor for accurate dependency learning and has not been largely studied by existing work before. Experiments are carried out on a dataset obtained in a trident roadway. The results demonstrate that the SFA-based method outperforms the sate of the art.