Efficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning

TL;DR

This paper introduces a channel pruning method for multi-object tracking neural networks, significantly reducing model size and enabling real-time performance on resource-limited edge devices like Jetson Orin Nano, thus enhancing privacy and efficiency.

Contribution

We propose a novel neural network pruning technique tailored for MOT models, achieving up to 70% size reduction while maintaining high accuracy on edge devices.

Findings

Model size reduced by up to 70%.

Maintains high accuracy after pruning.

Improves real-time performance on Jetson Orin Nano.

Abstract

The advancement of multi-object tracking (MOT) technologies presents the dual challenge of maintaining high performance while addressing critical security and privacy concerns. In applications such as pedestrian tracking, where sensitive personal data is involved, the potential for privacy violations and data misuse becomes a significant issue if data is transmitted to external servers. To mitigate these risks, processing data directly on an edge device, such as a smart camera, has emerged as a viable solution. Edge computing ensures that sensitive information remains local, thereby aligning with stringent privacy principles and significantly reducing network latency. However, the implementation of MOT on edge devices is not without its challenges. Edge devices typically possess limited computational resources, necessitating the development of highly optimized algorithms capable of delivering real-time performance under these constraints. The disparity between the computational requirements of state-of-the-art MOT algorithms and the capabilities of edge devices emphasizes a significant obstacle. To address these challenges, we propose a neural network pruning method specifically tailored to compress complex networks, such as those used in modern MOT systems. This approach optimizes MOT performance by ensuring high accuracy and efficiency within the constraints of limited edge devices, such as NVIDIA's Jetson Orin Nano. By applying our pruning method, we achieve model size reductions of up to 70% while maintaining a high level of accuracy and further improving performance on the Jetson Orin Nano, demonstrating the effectiveness of our approach for edge computing applications.