Scene-specific pedestrian detection based on transfer learning and saliency detection for video surveillance

Abstract

Pedestrian detection is a fundamental problem in video surveillance and has achieved great progress in recent years. However the performance of a generic pedestrian detector trained on some public datasets drops significantly when it is applied to some specific scenes due to the difference between source training samples and pedestrian samples in target scenes. We propose a novel transfer learning framework, which automatically transfers a generic detector to a scene-specific pedestrian detector without manually labeling training samples from target scenes. In our method, we get initial detected results and several cues are used to filter target templates whose labels we are sure about from the initial detected results. Gaussian mixture model (GMM) is used to get the motion areas in each video frame and some other target samples. The relevancy between target samples and target templates and the relevancy between source samples and target templates are estimated by sparse coding and later used to calculate the weights for source samples and target samples. Saliency detection is an essential work before the relevancy computing between source samples and target templates for eliminating interference of non-salient region. We demonstrate the effectiveness of our scene-specific detector on a public dataset, and compare with the generic detector. Detection rates improves significantly, and also it is comparable with the detector trained by a lot of manually labeled samples from the target scene.