Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors
<|reference_start|>Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors: 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.<|reference_end|>
@article{cheng2024deep,
    title={Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave
  Radar and Camera Sensors},
    author={Lei Cheng, Arindam Sengupta, and Siyang Cao},
    journal={arXiv preprint arXiv:2407.08049},
    year={2024},
    doi={10.1109/TITS.2024.3421339},
    archivePrefix={arXiv},
    eprint={2407.08049},
    primaryClass={cs.CV cs.SY eess.SP eess.SY}
}