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.