Pedestrian Tracking Algorithm for Video Surveillance Based on Lightweight Convolutional Neural Network

Abstract

The Efficient Convolution Operators for Tracking (ECO) algorithm has garnered considerable attention in both academic research and practical applications due to its remarkable tracking efficacy, yielding exceptional accuracy and success rates in various challenging contexts. However, the ECO algorithm heavily relies on the deep learning Visual Geometry Group (VGG) network model, which entails complexity and substantial computational resources. Moreover, its performance tends to deteriorate in scenarios involving target occlusion, background clutter, and similar challenges. To tackle these issues, this study introduces a novel enhancement to the pedestrian tracking algorithm. Specifically, the VGG network is substituted with a lightweight MobileNet v2 model, thereby reducing computational demands. Additionally, a Double Attention Networks (A2-Net) module is incorporated to augment the extraction of crucial information, while pre-training techniques are integrated to expedite model convergence. Experimental results demonstrate that the C-ECO algorithm achieves comparable accuracy and success rates to the conventional ECO algorithm, despite reducing the model size by 27.96% and increasing the tracking frame rate by 46.11%. Notably, when compared to other prevalent tracking algorithms, the C-ECO algorithm exhibits an accuracy of 82.20% and a success rate of 64.72%. These findings underscore the enhanced adaptability of the C-ECO algorithm in complex environments, offering a more lightweight model while delivering superior tracking capabilities.

Keywords

• Machine vision
• target tracking
• deep learning
• efficient convolution operator
• pedestrian tracking