Robust human detection under occlusion by integrating face and person detectors

Human detection under occlusion is a challenging problemin computer vision. We address this problem through a framework which
integrates face detection and person detection. We first investigate how
the response of a face detector is correlated with the response of a person
detector. From these observations, we formulate hypotheses that capture
the intuitive feedback between the responses of face and person detectors
and use it to verify if the individual detectors’ outputs are true or false.
We illustrate the performance of our integration framework on challeng-
ing images that have considerable amount of occlusion, and demonstrate
its advantages over individual face and person detectors.