A Computationally Effective Pedestrian Detection using Constrained Fusion with Body Parts for Autonomous Driving
A Computationally Effective Pedestrian Detection using Constrained Fusion with Body Parts for Autonomous Driving
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DOI：
10.1109/irc52146.2021.00024
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
M. Islam;Abdullah Al Redwan Newaz;Renran Tian;A. Homaifar;A. Karimoddini
中科院分区：
文献类型：
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作者：
M. Islam;Abdullah Al Redwan Newaz;Renran Tian;A. Homaifar;A. Karimoddini
本文采用一种增强的目标检测方法来解决行人的检测问题。本文特别考虑了自动驾驶场景中遮挡行人的检测问题，其中精度和速度之间的性能平衡至关重要。现有的研究主要集中在学习独立于身体部位语义的独特的人的表征。为了实现实时性能和鲁棒性检测，我们引入了一种基于身体部位的行人检测架构，其中身体部位通过计算有效的约束优化技术融合。我们证明了我们的方法显著提高了检测精度，同时增加了可以忽略不计的运行时开销。我们使用真实世界的数据集来评估我们的方法。实验结果表明，该方法优于现有的行人检测方法。
This paper addresses the problem of detecting pedestrians using an enhanced object detection method. In particular, the paper considers the occluded pedestrian detection problem in autonomous driving scenarios where the balance of performance between accuracy and speed is crucial. Existing works focus on learning representations of unique persons independent of body parts semantics. To achieve a real-time performance along with robust detection, we introduce a body parts based pedestrian detection architecture where body parts are fused through a computationally effective constraint optimization technique. We demonstrate that our method significantly improves detection accuracy while adding negligible runtime overhead. We evaluate our method using a real-world dataset. Experimental results show that the proposed method outperforms existing pedestrian detection methods.