Head detection algorithm based on improved FaceBoxes

For the vertical perspective of surveillance video, human body occlusion and background blur often occur, which lead to low accuracy of human body detection.This paper proposes a surveillance video number statistics method that takes human head as the target to detect human body. This method improves a lightweight network of FaceBoxesand adds multi-scale feature fusion to improve the accuracy of dense head detection. In the surveillance video, the head target is relatively small, and generally there is no large target. Therefore, k-means method is used to cluster the scale of candidate frame, and then to train, and overlapping region detection method is used to improve the accuracy of prediction frame. The experimental results show that the improved FaceBoxesmethod is about 4% more accurate and slightly slower than the previous one.