Autonomous Pedestrian Detection model for Crowd Surveillance using Deep Learning Framework

Abstract Pedestrian detection is a key ability required by most computer visions and crowd surveillance applications, with several applications such as person identification, person count and tracking. The number of techniques to identifying pedestrians in images has gradually increased in recent years, even with the significant advances in the state-of-the-art D based framework for object detection model. The research in the field of object detection and image classification has made a stride in the level of accuracy greater than 99% and the level of granularity. A powerful Object Detector, specifically designed for high-end surveillance applications, is needed that will not only position the bounding box and label it, but will also return their relative positions. The size of these bounding boxes can vary depending on the object and how it interacts with the physical world. To address these requirements, an extensive evaluation of the state-of-the-art algorithms has been presented in this paper. The work presented in this paper performs detections on MOT20 dataset using various algorithms and testing on a custom dataset recorded in our organization premises using an Unmanned Aerial Vehicle (UAV). Further, the experimental results are performed on Faster-RCNN, SSD and YOLO models. The Yolov5 model is found to outperform all other models with 61% precision and 44% of F measure value.