| Summary: | A surveillance system using object tracking enhances security by continuously monitoring and analyzing movements. However, tracking human movement in surveillance systems still presents challenges, especially in crowded environments. These challenges, such as occlusion, similar appearance, and deformation, can affect the accuracy and precision of object tracking. To address these issues, we introduced a new approach combining HSV-template matching with the MEESPSO algorithm. In this approach, HSV-template matching continuously detects the target object in sequence images, while the Midrange Exploration Exploitation Searching Particle Swarm Optimization (MEESPSO) algorithm searches for the target location in the frames. The proposed method was tested using six video datasets from Performance Evaluation of Tracking and Surveillance 2009 (PETS09) and Multiple Object Tracking Challenge 2020 (MOT20), encountering crowded environments that introduced challenges like occlusion, similar appearance, and deformation. Experiments on both PETS09 and MOT20 datasets demonstrated that the proposed method improved tracking performance by over 4.67% in accuracy and 15% in precision compared to existing studies, where the result effectively addresses the crowded environment challenges identified in this research.
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