OPTICAL FLOW BASED LEARNING APPROACH FOR ABNORMAL CROWD ACTIVITY DETECTION WITH MOTION DESCRIPTOR MAP

摘要

Automated abnormal crowd activity detection with faster execution time has been a major research issue in recent years. In this work, a novel method for detecting crowd abnormal activities is proposed which is based on processing of optical flow as motion parameter for machine learning. The proposed model makes use of magnitude vector which represents motion magnitude of a block in eight directions divided by a 45 degree pace angle. Further, motion characteristics are processed using Motion Descriptor Map (MDP), which takes two main parameters namely aggregate magnitude of motion flow in a block and Euclidean distance between blocks. Here, the angle of deviation between any two blocks determines which among the eight values in the magnitude vector to be considered for further processing. The algorithm is tested with two standard datasets namely UMN and UCSD Datasets. Apart from these the system is also tested with a custom dataset. On an average, an overall accuracy of 98.08% was obtained during experimentation.