Improving Autonomous Driving Perception: A Multi-Object Tracking Framework with CNN and DeepSORT
Chahira Kezzal, Selsabil Benderradji, Azeddine Benlamoudi, Salah Eddine Bekhouche · 2025
Multi-Object Tracking (MOT) is a crucial component of autonomous driving, enabling vehicles to perceive and predict dynamic environments accurately. In this work, we propose a multi-object tracking framework that integrates a CNN-based YOLOv11 detector with the DeepSORT tracker under the Tracking By Detection paradigm. To the best of our knowledge, this is the first integration of YOLOv11 and DeepSORT in a 2D Intelligent Transportation Systems (ITS) tracking pipeline. Our method enhances tracking performance by leveraging YOLOv11’s advanced feature extraction for high-precision object detection, while DeepSORT ensures robust identity preservation across frames. Our approach demonstrates superior tracking performance in complex urban driving scenarios, surpassing existing state-of-the-art methods. Specifically, our model achieves a MOTA of 56.61% and an HOTA of 56.28 % on the KITTI benchmark dataset, showcasing significant improvements in tracking accuracy.