Moving Pedestrian Localization and Detection With Guided Filtering

Abstract

Detecting a moving pedestrian is still a challenging task in a smart surveillance system due to dynamic scenes. Locating and detecting the moving pedestrian simultaneously influences the development of an integrated but low-resource smart surveillance system. This paper proposes a novel approach to locating and detecting moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv2, YOLOv3, and YOLOv4 within the provided ROI. Our proposed method resulted in more processing frames per second compared with previous approaches. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. It costs around ~50 fps in CPU to classify moving pedestrians and maintain a highly accurate rate. Due to its fast processing, the proposed approach is suitable for IoT or smart surveillance device which has limited resource.

Keywords

• Moving object analysis
• pedestrian localization and detection
• convolutional neural network (CNN)
• integrated surveillance system
• YOLO