Abstract
Environment perception is an important issue for autonomous driving applications. Vehicle detection and tracking is one of the most serious challenges and plays a crucial role for environment perception. Considering that the convolutional neural network (CNN) can provide high recognition rate for object detection, the vehicles are detected by utilizing Yolo v3 algorithm trained on ImageNet and KITTI datasets. Then, the detected multiple vehicles are tracked based on the combination of Kalman filter and data association strategy. Experiments on the publicly available KITTI object tracking datasets are conducted to test and verify the proposed algorithm. Results indicate that the proposed algorithm can achieve stable tracking under normal conditions even when the object is temporarily occluded.
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This work was supported by the National Natural Science Foundation of China under grant 51575079, the Doctoral Scientific Research Foundation of Liaoning Province under grant 20170520194 and the China Postdoctoral Science Foundation under Grant 2018M641688.
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Guo, L., Ge, P., He, D., Wang, D. (2019). Multi-vehicle Detection and Tracking Based on Kalman Filter and Data Association. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11744. Springer, Cham. https://doi.org/10.1007/978-3-030-27541-9_36
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DOI: https://doi.org/10.1007/978-3-030-27541-9_36
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