Pedestrian detection based on sparse coding and transfer learning

Pedestrian detection is a fundamental problem in video surveillance and has achieved great progress in recent years. However, training a generic detector performing well in a great variety of scenes has proved to be very difficult. On the other hand, exhausting manual labeling efforts for each specific scene to achieve high accuracy of detection is not acceptable especially for video surveillance applications. To alleviate the manual labeling efforts without scarifying accuracy of detection, we propose a transfer learning framework based on sparse coding for pedestrian detection. In our method, generic detector is used to get the initial target samples, and then several filters are used to select a small part of samples (called as target templates) from the initial target samples which we are very sure about their labels and confidence values. The relevancy between source samples and target templates and the relevancy between target samples and target templates are estimated by sparse coding and later used to calculate the weights for source samples and target samples. By adding the sparse coding-based weights to all these samples during re-training process, we can not only exclude outliers in the source samples, but also tackle the drift problem in the target samples, and thus get a well scene-specific pedestrian detector. Our experiments on two public datasets show that our trained scene-specific pedestrian detector performs well and is comparable with the detector trained on a large number of training samples manually labeled from the target scene.