Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors

Autor:	Cheng, Lei, Sengupta, Arindam, Cao, Siyang
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Signal Processing Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
DOI:	10.1109/TITS.2024.3421339
Popis:	Autonomous driving holds great promise in addressing traffic safety concerns by leveraging artificial intelligence and sensor technology. Multi-Object Tracking plays a critical role in ensuring safer and more efficient navigation through complex traffic scenarios. This paper presents a novel deep learning-based method that integrates radar and camera data to enhance the accuracy and robustness of Multi-Object Tracking in autonomous driving systems. The proposed method leverages a Bi-directional Long Short-Term Memory network to incorporate long-term temporal information and improve motion prediction. An appearance feature model inspired by FaceNet is used to establish associations between objects across different frames, ensuring consistent tracking. A tri-output mechanism is employed, consisting of individual outputs for radar and camera sensors and a fusion output, to provide robustness against sensor failures and produce accurate tracking results. Through extensive evaluations of real-world datasets, our approach demonstrates remarkable improvements in tracking accuracy, ensuring reliable performance even in low-visibility scenarios. Comment: Published in IEEE Transactions on Intelligent Transportation Systems
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.08049 Zobrazit plný text záznamu View this record from Arxiv