Abstract
In this paper, we propose a point-cloud-based millimeter-wave radar and camera fusion system, which aims to improve the recognition rate and tracking accuracy of the fusion system. For point cloud clustering, our method adds the velocity difference parameter to the density based spatial clustering of applications with noise (DBSCAN) algorithm and selects different parameter values according to three different kinds of targets. Aiming at the problem of inaccurate estimation of monocular ranging when the angle between the target and the sensor is formed, a distance-pixel-based algorithm is proposed to significantly reduce the above error. Experimental results show that the fusion system can improve the detection rate and tracking efficiency compared with the single camera scheme under the requirement of real-time.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (61731006), and was partly supported by the 111 Project No. B17008.
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Zhang, C., Li, S., Si, X., Liang, J. (2023). Target Recognition and Tracking Based on Point Cloud Fusion of Automotive Millimeter-Wave Radar and Camera. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2022. Lecture Notes in Electrical Engineering, vol 873. Springer, Singapore. https://doi.org/10.1007/978-981-99-1260-5_2
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DOI: https://doi.org/10.1007/978-981-99-1260-5_2
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