Object recognition from on-the-road traffic data

Said Muhammad; Qaisar Farooq; Chen Zuguo; Yimin Zhou

doi:10.1117/12.2662006

26 October 2022 Object recognition from on-the-road traffic data

Said Muhammad, Qaisar Farooq, Chen Zuguo, Yimin Zhou

Proceedings Volume 12452, 5th International Conference on Informatics Engineering and Information Science (ICIEIS 2022); 1245205 (2022) https://doi.org/10.1117/12.2662006
Event: 5th International Conference on Informatics Engineering and Information Science (ICIEIS 2022), 2022, Changsha, Hunan, China

Abstract

In order to detect the object and inspect the road conditions in real-time, the 2-dimensional (2D) and 3- dimensional (3D) data obtained from the onboard sensors, LiDAR and digital cameras are analyzed for object recognition to assist driving. Due to the uncertainties of the dynamic objects, such as pedestrians, animals or vibrated vehicles, extraction of complete and clear objects from LiDARs datasets requires complex post-processing since LiDAR data can be used for scanning at long distances, i.e., 300m, which can alarm the driver timely to take necessary actions. The dynamic and static objects from the LiDARs point clouds can be detected with the teacher-student framework algorithm along with the KITTI dataset. Furthermore, a semi-supervised theory is utilized to improve detection performance.

Citation Download Citation

Said Muhammad, Qaisar Farooq, Chen Zuguo, and Yimin Zhou "Object recognition from on-the-road traffic data", Proc. SPIE 12452, 5th International Conference on Informatics Engineering and Information Science (ICIEIS 2022), 1245205 (26 October 2022); https://doi.org/10.1117/12.2662006

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