A Real-Time Object Detection and Tracking Framework Based on RT-DETR and DeepSORT

Object detection and tracking are two critical tasks in computer vision, widely applied in security surveillance, autonomous vehicles, and behavioral analysis. Strong perfor- mance in object detection has been demonstrated by recent Transformer-based models, such as RT-DETR (Real-Time Detec- tion Transformer), due to their global context modeling and high accuracy. However, an inherent tracking mechanism is lacking in RT-DETR, which requires additional components to maintain identity consistency across frames. To address this limitation, an integration of RT-DETR with DeepSORT is proposed, leveraging the strengths of both models to enhance real-time object detection and tracking. A comparative evaluation with YOLOv8, a widely used real-time detector, is conducted to highlight the advantages of the proposed approach in tracking accuracy and robustness. Experiments show that effective performance is achieved in challenging scenarios such as object occlusion and intersection. Specifically, an IDF1 score of 60.0%, a MOTA of 42.4%, and a MOTP of 43.3% are obtained by RTl+DeepSORT on the MOT17-02-DPM dataset, outperforming YOLOv8x+DeepSORT. These results indicate that significant improvements in tracking accuracy are attained while real-time efficiency is maintained, making the proposed approach well-suited for applications such as intelligent surveillance and autonomous navigation.