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

TL;DR

This paper introduces a deep learning method that fuses radar and camera data to improve multi-object tracking in autonomous driving, enhancing accuracy and robustness in challenging conditions.

Contribution

It presents a novel fusion approach using LSTM and FaceNet-inspired features for robust multi-sensor object tracking in autonomous vehicles.

Findings

Significant accuracy improvements over existing methods

Robust performance in low-visibility scenarios

Effective sensor failure mitigation

Abstract

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.