Multi-object tracking algorithm based on improved YOLOv11 and adaptive DeepSORT

Abstract

This paper presents an improved multi-target tracking method based on YOLOv11 and adaptive DeepSORT, named RFCB-KF Tracker, aimed at addressing challenges such as small target loss and occlusion in multi-target tracking. The main contributions include the introduction of a novel attention mechanism, RFA, during the detection phase. This mechanism combines the convolutional block attention module and coordinated attention and features lightweight convolution operations, receptive field channel attention convolution and receptive field convolution with block attention modules, to enhance detection accuracy and efficiency. In the tracking phase, we improved the Kalman filter by expanding the state space from two dimensions to eight, optimizing the state transition and measurement matrices to effectively manage variations in target size and shape, thereby reducing estimation errors. In addition, we implemented an adaptive noise weight mechanism that dynamically adjusts noise parameters to enhance adaptability in diverse scenarios. Cholesky decomposition was utilized to optimize Kalman gain calculations, addressing numerical instability in complex motion contexts, and the Mahalanobis distance was introduced to effectively handle measurement errors. Experimental results demonstrate that RFCB-KF Tracker achieves improvements of 2.3% in MOTA, 0.9% in MOTP, 0.3% in IDF1, and a 5.6 frames per second increase in FPS on the MOT17 dataset, indicating a significant enhancement in multi-target pedestrian tracking performance. However, it is important to note that, while accuracy has improved, the increased algorithmic complexity may lead to slower computational speeds. Future research will focus on finding the optimal balance between accuracy and processing speed to meet real-time application requirements.