Low Resolution Camera for Human Detection and Tracking

This paper presents a robust and computationally efficient method for human detection and tracking. The unique feature of this method is that it has dedicated threads for human detection and camera control for human tracking. Moreover, it works with infra-red on and infra-red off. The method consists of five parts – training image acquisition, background subtraction, feature extraction, system training, and system testing. Firstly, some sample video clips have been taken with an IP camera for initial system implementation. The clips are then filtered to separate background and foreground. After that, some morphological operations are carried out to identify the most significant motion in the foreground. Those parts are cropped with some extra area and used to train a multiclass support vector machine (SVM) along with an image subset of the people detection dataset of The National Institute for Research in Computer Science and Control (French: Institut National de Recherche en Informatique et en Automatique, INRIA). A total of 597 images have been used as positive images and a total of 662 images have been used as negative images. Average detection accuracy of the system without infra-red is 89.37% and average detection accuracy of the system with infra-red is 72.66%. Therefore the average detection accuracy is 81.1%. We conclude (using dependent probabilistic analysis) that our system performs on an average of 89.37% accuracy based on our frame based analysis of video feeds. Keywords—human tracking; computer vision; surveillance; background subtraction; HOG