Enhanced face detection using body part detections for wearable cameras
With the recent broad acceptance of body worn cameras (BWC) for police departments, there is an increased need to perform video analytics for this domain. However, body worn cameras pose several challenges including severe motion blur, barrel camera distortion from wide angle lenses, close proximity and odd viewing angles, and poor lighting conditions. In this paper, we evaluate the performance of several state of the art face detection approaches including Aggregate Channel Features [1] and Faster R-CNN [2] and show their limitations in this domain. We then describe how face detection can be improved for BWC by corroborating information from body parts detection. We design a system using 0–1 linear integer programming to optimize the matching of body parts detections for each person in the scene and to maximize the hit rate of faces with supporting evidence. By leveraging information from body parts detection, we are able to improve the average precision by nearly two percent.