An Improved Pedestrian Detection Algorithm using Integration of Resnet and Yolo V2

Abstract

Pedestrian detection is one of the important tasks in object detection technology. The pedestrian detection algorithm has been used in applications like intelligent video surveillance, traffic analysis, and autonomous driving. In recent years, many pedestrian detection algorithms have been proposed but the key drawback is the accuracy and speed, which can be improved my integrating efficient algorithms. The proposed model improves the pedestrian detection algorithm by integrating two efficient algorithms together. The model is developed using the joint version of ResNet and YOLO v2, which preforms feature extraction and classification respectively. By using this model the efficiency of the system is increased by improving the accuracy rate so that it can be used with real-time applications. The model has been compared with existing models like SSD, Faster R-CNN and Mask R-CNN. Comparing with these models, the proposed model provides mAP value higher than these existing models with less loss function when tested on the INRIA dataset.