Boosting Online 3D Multi-Object Tracking through Camera-Radar Cross Check

• URL: http://arxiv.org/abs/2407.13937v1
• Date: Thu, 18 Jul 2024 23:32:27 GMT
• Title: Boosting Online 3D Multi-Object Tracking through Camera-Radar Cross Check
• Authors: Sheng-Yao Kuan, Jen-Hao Cheng, Hsiang-Wei Huang, Wenhao Chai, Cheng-Yen Yang, Hugo Latapie, Gaowen Liu, Bing-Fei Wu, Jenq-Neng Hwang,
• Abstract summary: CRAFTBooster is a pioneering effort to enhance radar-camera fusion in the tracking stage, contributing to improved 3D MOT accuracy. The superior experimental results on the K-Radaar dataset, which exhibit 5-6% on IDF1 tracking performance gain, validate the potential of effective sensor fusion in advancing autonomous driving.
• Score: 24.764602040003403
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the domain of autonomous driving, the integration of multi-modal perception techniques based on data from diverse sensors has demonstrated substantial progress. Effectively surpassing the capabilities of state-of-the-art single-modality detectors through sensor fusion remains an active challenge. This work leverages the respective advantages of cameras in perspective view and radars in Bird's Eye View (BEV) to greatly enhance overall detection and tracking performance. Our approach, Camera-Radar Associated Fusion Tracking Booster (CRAFTBooster), represents a pioneering effort to enhance radar-camera fusion in the tracking stage, contributing to improved 3D MOT accuracy. The superior experimental results on the K-Radaar dataset, which exhibit 5-6% on IDF1 tracking performance gain, validate the potential of effective sensor fusion in advancing autonomous driving.

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