Crowd Detection by Video Processing

Abstract

The video may consist of multiple shots. The target in the probe video is only annotated once with a face bounding box in a frame. Most video face identification techniques assume that the video is of single shot, and thus the bounding boxes of the target face can be extracted by tracking a face across the video frames. Nevertheless, such automatic annotation is vulnerable to the drifting of the face tracker, and the face tracking algorithm is inadequate to associate the face images of the target across multiple shots. A target face association (TFA) technique retrieves a set of representative face images in a given video that are likely to have the same identity as the target face. These face images are then utilized to construct a robust face representation of the target face for searching the corresponding subject in the gallery. Since two faces that appear in the same video frame cannot belong to the same person, such cannot-link constraints are utilized for learning a target-specific linear classifier for establishing the intra/ inter-shot face association of the target. Experimental results on the newly released JANUS challenge set 3 (JANUS CS3) dataset show that TFA method generates robust representations from target-annotated videos and demonstrates good performance for the task of video-based face identification problem.