A Computer Vision Framework for Detecting and Preventing Human-Elephant Collisions

Pushkar Shukla, Isha Dua, Balasubramanian Raman, Ankush Mittal; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2883-2890

Human Elephant Collision (HEC) is a problem that is quite common across many parts of the world. There have been many incidents in the past where conflict between humans and elephants has caused serious damage and resulted in the loss of lives as well as property. The paper proposes a frame-work that relies on computer vision approaches for detecting and preventing HEC. The technique initially recognizes the areas of conflict where accidents are most likely to occur. This is followed by elephant detection system that identifies an elephant in the video frame. Two different algorithms to detect the presence of elephants having a mean average precision of 98.621% and 97.667% have been proposed in the paper. The position of the elephant once detected is tracked with respect to the area of conflict with a particle filter. A warning message is displayed as soon as the position of the elephant overlaps with the area of conflict. The results of the techniques that were applied on videos were discussed in the paper.