Occluded pedestrian detection through bi-center prediction in anchor-free network

To handle the occlusion issue, this paper proposes a simple but effective pedestrian detector who designs a novel bi-center prediction mechanism (namely Bi-Center) based on the anchor-free network. Specifically, we modify the training loss or ground truth of the two designed center prediction branches in the pedestrian detection head. In the first branch, the negative samples around centers of occluded pedestrians, which are mainly covered by the background region, are down-weighted in a novel weighted Focal Loss according to the occlusion levels. In the second branch, we further integrate the occlusion ratio into the Gaussian mask of center ground truth to improve the predicted probability of occluded pedestrian instances. We optimize the designed detection head of the network in an end-to-end fashion, and utilize the complementary outputs of the designed two center prediction branches to boost the detection performance. The proposed bi-center prediction mechanism forces the network to pay more attention to the occluded pedestrian instances. Experimental results on the challenging CityPersons, Caltech, and CrowdHuman benchmarks sufficiently validate the efficacy of our Bi-Center detector for occlusion handling in pedestrian detection.