Human detection and tracking in video surveillance system
Krishnan Sriram, Rohini Havaldar · 2016
The human detection and tracking in a video plays major roll in security systems. This paper proposes an approach to detect and track the persons in a video. This approach uses Gaussian Mixture Model to detect the person and Kalman filter to track the detected person. The processing time to detect the person is reduced by performing the detection operation on down-sampled video. After detecting the person, the original size of the video is reconstructed using Papoulis-Gerchberg method. The performance analysis is carried out by comparing with the state-of-the-art-algorithms. The experimental results show that the proposed method is well suited for detecting and tracking the person in lower processing time.