计算机科学
恶劣天气
人工智能
能见度
深度学习
跳跃式监视
目标检测
卷积神经网络
标杆管理
计算机视觉
跟踪(教育)
传感器融合
模式识别(心理学)
地理
心理学
业务
气象学
营销
教育学
作者
M. Hassaballah,Mourad A. Kenk,Khan Muhammad,Shervin Minaee
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2021-07-01
卷期号:22 (7): 4230-4242
被引量:78
标识
DOI:10.1109/tits.2020.3014013
摘要
Vehicle detection and tracking play an important role in autonomous vehicles and intelligent transportation systems. Adverse weather conditions such as the presence of heavy snow, fog, rain, dust or sandstorm situations are dangerous restrictions on camera’s function by reducing visibility, affecting driving safety. Indeed, these restrictions impact the performance of detection and tracking algorithms utilized in the traffic surveillance systems and autonomous driving applications. In this article, we start by proposing a visibility enhancement scheme consisting of three stages: illumination enhancement, reflection component enhancement, and linear weighted fusion to improve the performance. Then, we introduce a robust vehicle detection and tracking approach using a multi-scale deep convolution neural network. The conventional Gaussian mixture probability hypothesis density filter based tracker is utilized jointly with hierarchical data associations (HDA), which splits into detection-to-track and track-to-track associations. Herein, the cost matrix of each phase is solved using the Hungarian algorithm to compensate for the lost tracks caused by missed detection. Only detection information (i.e., bounding boxes with detection scores) is used in HDA without visual features information for rapid execution. We have also introduced a novel benchmarking dataset designed for research in applications of autonomous vehicles under adverse weather conditions called DAWN. It consists of real-world images collected with different types of adverse weather conditions. The proposed method is tested on DAWN, KITTI, and MS-COCO datasets and compared with 21 vehicle detectors. Experimental results have validated effectiveness of the proposed method which outperforms state-of-the-art vehicle detection and tracking approaches under adverse weather conditions.
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