Abstract: In the pedestrian detection of intelligent video monitoring system, the current target detection algorithms R-CNN and YOLO series are slower, which cannot meet the real-time requirements, or require large GPU memory space, which is difficult to deploy. YOLOv3-tiny algorithm, as a simplified version of YOLO series, has less requirements for equipment and is faster, but its accuracy is low. In this paper, the number of horizontal and vertical directions of YOLOv3-tiny algorithm grid cell is adjusted, the network structure of YOLOv3-tiny algorithm is optimized, and the number and size of anchors are determined by clustering, so as to obtain the improved YOLO-Y algorithm, and expand the training data set by data enhancement method. The improved YOLO-Y algorithm improves the mAP from 90% to 92%, Recall from 95% to 97%, detection speed up to 26 frames/s, occupies about 1 GB of video memory space. The experimental results show that the improved YOLO-Y algorithm significantly improves the detection accuracy of the algorithm, has real-time performance, and does not need too much memory space to meet the requirements of most intelligent video monitoring systems.

Key words: intelligent video monitoring system, pedestrian detection, YOLOv3-tiny, clustering, deep learning, pedestrian dataset