Extended Hierarchical Temporal Memory for Motion Anomaly Detection

摘要

This paper describes the application of hierarchical temporal memory (HTM) to the task of anomaly detection in human motions. A number of model experiments with well-known motion dataset of Carnegie Mellon University have been carried out. An extended version of HTM is proposed, in which feedback on the movement of the sensor's focus on the video frame is added, as well as intermediate processing of the signal transmitted from the lower layers of the hierarchy to the upper ones. By using elements of reinforcement learning and feedback on focus movement, the HTM's temporal pooler includes information about the next position of focus, simulating the human saccadic movements. Processing the output of the temporal memory stabilizes the recognition process in large hierarchies.