Adaptive scene correlation learning based on scale-invariant appearance co-occurrence model for camera network

It is a significant problem to learn the scene correlation of uncalibrated static cameras, which can be applied for intelligent surveillance systems with a large scale camera network. Some existing approaches learn the scene correlation among camera views by tracking targets across cameras. They seldom analyze the scene correlation among cameras with appearance modeling for moving targets. In this paper, a novel adaptive appearance co-occurrence modeling approach is proposed to learn the scene correlation by long term statistics. Firstly, spatial-depth scale-invariant model (SDSI) is introduced to make a scale normalization for the whole camera views. A uniform metric of target scales is established in the system so that an absolute height is obtain to make an identification of moving targets with similar appearance. Then, the appearance co-occurrence modeling is formulated to learn the spatio-temporal co-occurrence relationship among cameras with detection for targets with same appearance in temporal neighbourhood. The proposed approach generates visual attention cross a number of camera views in case that the cameras are not calibrated, which is adaptive to learn the co-occurrence correlation. The effectiveness of our approach is demonstrated in PKU-SES system with 10 cameras in two sites.