关键词: 人头检测, 多尺度融合, K-means, 有效感受野, 密集人数统计

Abstract:

In order to improve the accuracy and speed of people counting in video monitoring and solve the problem of high occlusion caused by clothing blocking in traditional human body detection, an improved lightweight head detection MKYOLOv3-tiny is proposed. This method is an improvement on YOLOv3-tiny network. For improving the detection accuracy, multi-scale fusion is carried out on the head features of lower layer to realize classification prediction and position regression of different convolution layers. For improving anchors accuracy, K-means clustering algorithm is adopted to reduce initial anchors specification, according to the characteristics of small human heads and the idea of effective receptive field. Experimental results show that the detection accuracy of the improved model on Brainwash dataset increases by 3.21%, and the miss detection rate reduces by 8.7.

Key words: head detection, multi-scale fusion, K-means, effective receptive field, crowd counting