Knowledge Commons of Institute of Automation,CAS
A Temporal-based Deep Learning Method for Multiple Objects Detection in Autonomous Driving | |
Chen, Yaran1,2![]() ![]() ![]() ![]() | |
2018-07 | |
会议名称 | 2018 International Joint Conference on Neural Networks |
会议日期 | 8-13 July 2018 |
会议地点 | Rio de Janeiro, Brazil |
摘要 | This paper proposes a novel vision-based object detection method in autonomous driving, which introduces the temporal information into the deep learning-based detection method for moving object detection. Vision-based object detection is a critical technology for autonomous driving. The objects in the real world such as driving cars, don't have great changes in their positions and velocities. So the position change of objects between two consecutive frames is not large. This is usually ignored by traditional works, which usually use object detection methods on still-images to detect moving objects. Considering the relationship among consecutive frames (temporal information), we present a robust and real-time tracking method following image detection to refine the object detection results. Based on the three key attributes (distances, sizes and positions), the tracking method aims to build the association between the detected objects on the current frame and those in previous frames. The proposed object detection with temporal information dramatically improves the performance of existing object detection algorithms based on stillimage. With the proposed method, we won the champion in the preceding vehicle detection task in 2017 intelligent vehicle future challenge(2017 IVFC). |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23521 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Beijing Normal University |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chen, Yaran,Zhao, Dongbin,Li, Haoran,et al. A Temporal-based Deep Learning Method for Multiple Objects Detection in Autonomous Driving[C],2018. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2018.IJCNN_VehicleDe(2116KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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