Aggressive movement detection using optical flow features base on digital & thermal camera

Detection and tracking of people in digital images has been subject to extensive research in the past decades.Following the growing availability of thermal cameras and the distinctive thermal signature of humans, research effort has been focusing on developing people detection and tracking methodologies applicable to this sensing modality.Thermal imaging technology can be used to detect aggressive levels in humans based on the radiated heat from their face and body. Previous research proposed an approach to figure out human aggressive features using Horn-Schunck optical flow algorithm in order to find the flow vector for all video frames using digital camera only. However, still not strong enough to confirm and verify the existence of an aggressive movement. Then, we propose another approach using thermal videos to detect aggressive features in human aggressive movement.Video frames are collected using thermal camera and then extracted into thermal images. This research also guides and discovers the patterns of body distracted movement.Result below will show the comparison between both cameras digital and thermal camera

Implementing low level features for human aggressive movement detection

In this real world, being able to identify the signs of imminent abnormal behaviors such as aggression or violence and also fights, is of extreme importance in keeping safe those in harm’s way. This research propose an approach to figure out human aggressive movements using Horn-Schunck optical flow algorithm in order to find the flow vector for all video frames. The video frames are collected using digital camera. This research guides and discovers the patterns of body distracted movement so that suspect of aggression can be investigated without body contact. Using the vector of this method, the abnormal and normal video frames are then classified and utilized to define the aggressiveness of humans. Preliminary experiment result showed that the low level of feature extraction can classify human aggressive and non-aggressive movements