Title: Deep learning approach to pedestrian detection and path prediction

Authors: Ujwalla Gawande; Kamal Hajari; Yogesh Golhar

Addresses: Department of Information Technology, Yeshwantrao Chavan College of Engineering, Hingna Road, Wanadongri, Nagpur, Maharastra – 441110, India ' Department of Information Technology, Yeshwantrao Chavan College of Engineering, Hingna Road, Wanadongri, Nagpur, Maharastra – 441110, India ' Department of Computer Engineering, St. Vincent Palloti College of Engineering and Technology, Gavsi Manapur, Wardha Road, Nagpur, Maharashtra – 441108, India

Abstract: Pedestrian detection and path prediction are significant challenges in vision-based surveillance systems. Because of variances in pedestrian postures and scales, backdrops, and occlusion, advanced computer vision applications face many obstacles. To address these issues, we provide an improved YOLOv5 pedestrian recognition and path prediction model. To begin, the revised YOLOv5 model is employed to determine pedestrians of varied sizes and proportions. A pedestrian's path is estimated using a path prediction approach. The proposed method addresses partial occlusion situations in order to reduce object occlusion-induced progression and loss, as well as linking recognition results to motion properties. The route prediction system then analyses motion and directional data to estimate the direction of pedestrian movement. The results of the experiments significantly enhanced the performance on datasets from Caltech, INRIA, MS COCO, ETH, KITTI, and the proposed pedestrian dataset. Improved YOLOv5 outperforms existing methods. The Caltech dataset has the lowest log-average miss rate (8.32%), followed by the INRIA dataset (7.32%) and the ETH dataset (32.64%). Results from the KITTI dataset were promising, at 76%, 64%, and 60%. 8.69% miss rate on the proposed pedestrian dataset and 8.57% on the MS COCO dataset. Finally, we conclude and look into future research.

Keywords: convolutional neural network; CNN; deep learning; YOLOv5; pedestrian detection; tracking; path prediction.

DOI: 10.1504/IJCVR.2024.141815

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.6, pp.693 - 714

Received: 26 Mar 2022
Accepted: 13 Apr 2023
Published online: 02 Oct 2024 *

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