Low-FPS Multi-Object Multi-Camera Tracking via Deep Learning

Abstract

Multi-Object Multi-Camera Tracking (MOMCT) is essential for real-world applications but remains challenging in low-FPS environments where motion-based tracking is ineffective. This study proposes a novel vehicle tracking approach that integrates YOLOv9 for object detection, SwinReID for feature extraction, and a KNN-based matching algorithm to enhance tracking across multiple cameras. Our research leverages the AICUP 2024 Spring Competition dataset, which includes surveillance footage from Chiayi City, along with ImageNet for model training. To improve the accuracy, we incorporate scene segmentation to reduce false detections and optimize the buffer size per camera through an exhaustive search, adapting to varying perspectives and frame rates. Our method achieves a total score of 1.263626 based on IDF1 and MOTA metrics, demonstrating its effectiveness in low-FPS settings. These results contribute to the development of more robust MOMCT systems for traffic monitoring and surveillance.