Illegal Trash Thrower Detection Based on HOGSVM for a Real-Time Monitoring System

Abstract

Some people are too ignorant of their surroundings. Most of them, usually, throw trash illegally in the streets or abandoned areas. In this paper, to get rid of such a problem, we have proposed a real-time monitoring mechanism to find the illegal trash throwing person for an intelligent surveillance system. The proposed mechanism of finding people and trash from the video frame can be done using subsequently applying the following several steps. At first, we have applied the shadow removal technique and then applied the background subtraction procedure. To reduce the noise levels, we have implemented labeling & filtering operations. For extracting feature vectors, a histogram of oriented gradients (HOG) is used. Finally, by applying a support vector machine classifier (SVM), we have identified both humans and trash from the video frames. The key contribution of this proposed framework is to gain better performance by applying the shadow removal technique before the background subtraction procedure. Besides, the temporary bounding box concept has been taken into consideration during the trash detection phase.