Secure Multi-Object Detection and Tracking via YOLOv7 Integration with Particle Filter Algorithms

Multi-object detection and tracking are a fundamental task in computer vision, especially in surveillance, autonomous systems, and robotics. YOLOv7, a state-of-the-art real-time object detection model, offers high-speed performance and accuracy, which faces challenges in maintaining object identity over time. Existing tracking methods often suffer from drift, occlusion, and identity switching due to noise, dynamic environments, and lack of temporal consistency. To address these issues, here is the proposed SMODT-Y7PF (Secure Multi-Object Detection and Tracking via YOLOv7 with Particle Filter), a hybrid framework integrating YOLOv7 for robust detection and Particle Filter algorithms for probabilistic tracking and motion estimation. This combination improves identity preservation and resilience against occlusion and noise. The proposed method uses YOLOv7 to detect multiple objects in each frame and employs Particle Filters to maintain object trajectories by estimating the posterior distribution over time. This integration ensures secure and consistent tracking in real-time applications. Experimental results show that SMODT-Y7PF significantly improves tracking accuracy, reduces identity switches, and enhances robustness in dynamic and cluttered environments, making it ideal for smart surveillance and autonomous navigation systems.

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