An Efficient Pedestrian Detection for Realtime Surveillance Systems based on Modified YOLOv3

Pedestrian detection is an important branch of object detection due to its various applications. It plays a vital role in many fields such as intelligent surveillance systems. The recognition, identification and tracking modules of surveillance are based on efficient and accurate pedestrian detection. Our paper proposes an efficient model to solve real-time pedestrian detection with high accuracy based on modified ShuffleNet and YOLOv3 models. We provide a method to pick the dimensions and number of anchor boxes for predicting bounding boxes accurately. Then we use two improved shuffle units to lightweight the backbone of YOLOv3, which reduces the 67.5% floating point operations per second (FLOPs) and 65.1% parameters. We validate our model on CrowdHuman detection data set and get 62.7 mAP for face and 62.0 mAP person with 0.748 average IOU. Our network processes images in real-time at 186.1 frames per second for network and 12.5 frames per second for the entire model on CrowdHuman.