Title: Energy-aware vehicle/pedestrian detection and close movement alert at nighttime in dense slow traffic on Indian urban roads using a depth camera

Authors: N.S. Manikandan; K. Ganesan

Addresses: TIFAC-CORE, Vellore Institute of Technology (VIT), Vellore 632014, India ' School of Information Technology and Engineering, VIT Business School, Vellore Institute of Technology (VIT), Vellore 632014, India

Abstract: In recent times, infrared cameras, thermal cameras, RADAR, LIDAR, and depth cameras are widely used in the nighttime vehicle/pedestrian detection systems. In the present research, we propose a novel way of detecting the vehicles/pedestrians in dense, slow traffic conditions at night times. To train and build the necessary artificial intelligence model, daytime depth images are used. For training, the necessary datasets are created using a customised method. The trained model was used to detect the vehicles/pedestrians during night time. In addition, the tracking algorithm was used to follow the vehicle/pedestrian and predict its close movement and direction and provide the necessary warnings to the vehicle drivers. The proposed model was tested against a variety of object detection and tracking techniques involving embedded GPUs and depth cameras. The best suits of algorithms were identified based on the metrics such as accuracy, execution time, and the less carbon emission. Our proposed method has detected and tracked the vehicles/pedestrians accurately within the 17-metre range.

Keywords: green computing; artificial intelligence; deep learning; object detection; object tracking.

DOI: 10.1504/IJCVR.2023.134313

International Journal of Computational Vision and Robotics, 2023 Vol.13 No.6, pp.658 - 676

Received: 10 Nov 2021
Accepted: 08 Jun 2022
Published online: 18 Oct 2023 *

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