RETA: 4D Radar-Based End-to-End Joint Tracking and Activity Estimation for Low-Observable Pedestrian Safety in Cluttered Traffic Scenarios
RETA: 4D Radar-Based End-to-End Joint Tracking and Activity Estimation for Low-Observable Pedestrian Safety in Cluttered Traffic Scenarios
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DOI：
10.1109/tits.2023.3321463
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发表时间：
2024-05
影响因子：
8.5
通讯作者：
Zhenyuan Zhang;Huizhen Lai;Darong Huang;Xin Fang;Mu Zhou;Ying Zhang
中科院分区：
文献类型：
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作者：
Zhenyuan Zhang;Huizhen Lai;Darong Huang;Xin Fang;Mu Zhou;Ying Zhang
由于行人雷达散射截面（RCS）小，是基于雷达的汽车感知系统的典型低可观测交通参与者。行人活动的早期检测和理解对汽车安全具有重要意义。为此，本文提出了一种端到端的联合跟踪和活动估计（RETA）系统的基础上的4D汽车雷达，特别是处理在混乱的现实世界场景下的行人活动识别。首先，为了保证定位精度，提出了一种新型的综合检测与跟踪算法，该算法综合了所有未阈值的4D雷达测量值来探索多帧之间的空间相干信息，避免了弱目标信息的丢失。在此基础上，为了区分连续轨迹中持续时间不同的连续活动，本文创新性地提出了一种分解的连接主义递归卷积神经网络，该网络有助于融合时空运动特征提取。特别是，劳动消耗活动的预分割问题是规避与建议的神经网络中的连接主义的时间分类算法的帮助。最后，RETA可以实现真实的端到端感知应用。大量的实验结果表明，其优越性和有效性，达到94.8%的连续识别准确率。据我们所知，这是第一个专门针对低可观察性行人的端到端活动识别系统。在补充材料中上传了在具有挑战性的实际交通场景中录制的演示视频。
Due to the small radar cross Section (RCS), pedestrians are typical low-observable traffic participants for radar-based automotive perception systems. The early detection and understanding of pedestrians’ activities are of great significance to automotive safety. To this end, this paper presents an end-to-end joint tracking and activity estimation (RETA) system based on 4D automotive radar, which deals in particular with pedestrian activity identification under cluttered real-world scenes. Firstly, a novel integrated detection and tracking algorithm is proposed to guarantee positioning accuracy, in which all unthresholded 4D radar measurements are incorporated to explore the spatial coherent information across multiple frames, avoiding weak target information loss. After that, to discriminate continuous activities with varying durations in sequential trajectories, this paper innovatively presents a decomposed connectionist recurrent convolutional neural network, which facilitates fused temporal-spatial motion feature extraction. Especially, the labor-consuming activity pre-segmentation problem is circumvented with the help of a connectionist temporal classification algorithm in the proposed neural network. At last, RETA can be implemented for real end-to-end perception applications. Extensive experiment results highlight its superiority and effectiveness by attaining a continuous recognition accuracy of 94.8%. To the best of our knowledge, this is the first end-to- end activity recognition system specific for low-observable pedestrians. A demonstration video recorded in challenging practical traffic scenarios has been uploaded in the supplementary materials.