Online Adaptive Learning for Multi-camera People Counting

People counting has attracted much attention in video surveillance. This paper proposes an online adaptive learning people counting system across multiple cameras with partial overlapping Fields Of Views (FOVs). The main novelty of this system is that: 1) we propose an online adaptive learning scheme to detect and count people in order to make the system adaptive to various scenes. The system can online update the Gaussian Mixture Model (GMM) based classifier by collecting samples with high confidence automatically; 2) We present an approach to gather the number of people from multiple cameras. The system uses similarity measurement combined with homography transformation to find the corresponding people in overlapping FOVs and integrates the counting results of multiple cameras finally. Experimental results show that the proposed system can adapt to different scenes and count the pedestrians across multiple cameras accurately.