Adaptive object classification in surveillance system by exploiting scene context

Jitao Sang, Zhen Lei, Shengcai Liao, Stan Z. Li

Abstract: Surveillance system involving hundreds of cameras becomes very popular. Due to various positions and orientations of camera, object appearance changes dramatically in different scenes. Traditional appearance based object classification methods tend to fail under these situations. We approach the problem by designing an adaptive object classification framework which automatically adjust to different scenes. Firstly, a baseline object classifier is applied to specific scene, generating training samples with extracted scene-specific features (such as object position). Based on that, bilateral weighted LDA is trained under the guide of sample confidence. Moreover, we propose a Bayesian classifier based method to detect and remove outliers to cope with contingent generalization disaster resulted from utilizing high confidence but incorrectly classified training samples. To validate these ideas, we realize the framework into an intelligent surveillance system. Experimental results demonstrate the effectiveness of this adaptive object classification framework.