A Lightweight Surveillance System for Human Re-Identification Using Traditional Image Processing Techniques

Abstract

Surveillance systems play a pivotal role in enhancing security, especially in large-scale environments such as academic campuses. While modern re-identification systems typically employ deep learning techniques, these models demand significant computational resources and extensive labeled datasets, limiting their applicability in real-world low-resource environments. This study presents the Integrated Campus-Activities Monitoring System (ICAMS), a lightweight, real-time surveillance system for human re-identification based on traditional image processing methods. The proposed system integrates a revised Structural Similarity Index (SSIM) and a novel Luv Similarity metric within the CIELUV color space, eliminating the need for computationally expensive deep learning models. Experimental evaluations show that the system achieves satisfactory accuracy and robustness in appearance-based human tracking while maintaining low hardware requirements. These results demonstrate the potential for deploying cost-effective surveillance systems capable of real-time performance in environments with limited infrastructure.