Autonomous pedestrian detection for crowd surveillance using deep learning framework

Pedestrian detection is crucial for crowd surveillance applications and cyber-physical systems that can deliver timely and sophisticated solutions, especially with applications like person identification, person count, and tracking as the number of people rises. Even though the number of cutting-edge neural network-based frameworks for object detection models and pedestrian detection in images has steadily increased, object detection and image classification have made progress in terms of accuracy levels greater than 99% and level of granularity. A powerful object detector, specifically designed for high-end surveillance applications, is needed to position the bounding box, label it and return its relative positions. The size of these bounding boxes can vary depending on the object and its interaction with the physical world. To overcome these limitations and requirements, an extensive evaluation of the state-of-the-art algorithms has been presented in this paper. The work presented in this paper performs object detections on the MOT20 dataset using various algorithms and testing on a custom dataset recorded in our organization premises using an unmanned aerial vehicle (UAV). The main contribution of the paper is to tune the hyperparameters of the Yolov5 object detection algorithm to develop a new efficient, simple, and reliable object detector in Surveillance. The modified Yolov5 model outperforms the existing Faster-RCNN, SSD, and YOLO algorithms, according to real-world data experiments, and ensures better object detection with a 61% precision and 44% F measure value.