Pedestrian detection in the crowd is a challenging task because of intra-class occlusion. More prior information is needed for the detector to be robust against it. Human head area is naturally a strong cue because of its stable appearance, visibility and relative location to body. Inspired by it, we adopt an extra branch to conduct semantic head detection in parallel with traditional body branch. Instead of manually labeling the head regions, we use weak annotations inferred directly from body boxes, which is named as `semantic head'. In this way, the head detection is formulated into using a special part of labeled box to detect the corresponding part of human body, which surprisingly improves the performance and robustness to occlusion. Moreover, the head-body alignment structure is explicitly explored by introducing Alignment Loss, which functions in a self-supervised manner. Based on these, we propose the head-body alignment net (HBAN) in this work, which aims to enhance pedestrian detection by fully utilizing the human head prior. Comprehensive evaluations are conducted to demonstrate the effectiveness of HBAN on CityPersons dataset.

中文翻译：

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语义头部增强人群中的行人检测

由于类内遮挡，人群中的行人检测是一项具有挑战性的任务。检测器需要更多先验信息才能对其具有鲁棒性。人体头部区域自然是一个强有力的线索，因为它具有稳定的外观、可见性和与身体的相对位置。受此启发，我们采用额外的分支与传统的身体分支并行进行语义头部检测。我们没有手动标记头部区域，而是使用直接从主体框推断出的弱注释，称为“语义头部”。这样，头部检测被公式化为使用标记框的特殊部分来检测人体的相应部分，这令人惊讶地提高了性能和对遮挡的鲁棒性。此外，通过引入对齐损失，明确探索了头体对齐结构，它以自我监督的方式运作。基于这些，我们在这项工作中提出了头身对齐网络（HBAN），旨在通过充分利用人类头部先验来增强行人检测。进行了综合评估以证明 HBAN 在 CityPersons 数据集上的有效性。