Small-Scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation
Small-Scale Pedestrian Detection Based on Topological Line Localization and Temporal Feature Aggregation
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DOI：
10.1007/978-3-030-01234-2_33
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发表时间：
2018-09
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影响因子：
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通讯作者：
Tao Song;Leiyu Sun;Di Xie;Haiming Sun;Shiliang Pu
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文献类型：
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作者：
Tao Song;Leiyu Sun;Di Xie;Haiming Sun;Shiliang Pu
行人检测中的一个关键问题是检测小尺寸物体，这些物体会在图像和视频中引入微弱的对比度和运动模糊，我们认为这应该部分诉诸根深蒂固的注释偏见。出于这一动机，我们提出了一种新的方法，集成了体细胞拓扑线定位（TLL）和时间特征聚合检测多尺度行人，特别适用于小规模的行人，相对远离相机。此外，基于马尔可夫随机场（MRF）的后处理方案，以消除遮挡情况下的歧义。综合应用这些方法，我们在加州理工学院的基准测试中取得了最好的检测性能，并显着提高了小规模目标的性能（未命中率从74.53%下降到60.79%）。除此之外，我们还在CityPersons数据集上实现了具有竞争力的性能，并在KITTI数据集上显示了注释偏差的存在。
A critical issue in pedestrian detection is to detect small-scale objects that will introduce feeble contrast and motion blur in images and videos, which in our opinion should partially resort to deep-rooted annotation bias. Motivated by this, we propose a novel method integrated with somatic topological line localization (TLL) and temporal feature aggregation for detecting multi-scale pedestrians, which works particularly well with small-scale pedestrians that are relatively far from the camera. Moreover, a post-processing scheme based on Markov Ran-dom Field (MRF) is introduced to eliminate ambiguities in occlusion cases. Applying with these methodologies comprehensively, we achieve best detection performance on Caltech benchmark and improve performance of small-scale objects signicantly (miss rate decreases from 74.53% to 60.79%). Beyond this, we also achieve competitive performance on CityPersons dataset and show the existence of annotation bias in KITTI dataset.