The detection performance in crowd scenes is limited by recalling hard objects (e.g., occluded objects). It requires that this kind of objects can be successfully detected and retained by the non-maximum suppression (NMS) while controlling false positives. The existing dynamic label assignment algorithms can help recall these objects by adaptively allocating appropriate positive samples, however, they ignore the alignment with the selecting rules of NMS. This leads to the fact that detecting objects in crowd scenes are still very sensitive to the NMS threshold setting. As a result, the existing methods can only set a low NMS threshold to avoid the excessive false positives, causing some objects failed to be recalled. And these methods also generally lack more excitation for positive samples, which hinders further facilitating the recall of hard instances in crowd scenes. This article proposes a novel dynamic label assignment strategy for object detection in crowd scenes, callednon-maximum suppression guided label assignment(NGLA), which aligns the assignment strategy with NMS process and learns more prominent positive samples. Following NMS, NGLA introduces the IoU between samples with their corresponding best samples to define positive and negative samples. To cooperate with NGLA, anNMS-aware lossis proposed to dynamically assign sample weights when supervising sample predictions, which also considers the IoU with the best sample. In addition, for better classification prediction, aregression assisted classification branchis designed to help detectors perceive the relation between the regression predictions of each sample and the corresponding best sample. Experiments demonstrate that NGLA outperforms other label assignment methods on CrowdHuman and Citypersons, and is less sensitive to the NMS threshold in crowd scenes.

Occlusions, scale variation and numerous false positives still represent fundamental challenges in pedestrian detection. Intuitively, different sizes of receptive fields and more attention to the visible parts are required for detecting pedestrians with various scales and occlusion levels, respectively. However, these challenges have not been addressed well by existing pedestrian detectors. This paper presents a novel convolutional network, denoted as box guided convolution network (BGCNet), to tackle these challenges simultaneously in a unified framework. In particular, we proposed a box guided convolution (BGC) that can dynamically adjust the sizes of convolution kernels guided by the predicted bounding boxes. In this way, BGCNet provides position-aware receptive fields to address the challenge of large variations of scales. In addition, for the issue of heavy occlusion, the kernel parameters of BGC are spatially localized around the salient and mostly visible key points of a pedestrian, such as the head and foot, to effectively capture high-level semantic features to help detection. Furthermore, a local maximum (LM) loss is introduced to depress false positives and highlight true positives by forcing positives, rather than negatives, as local maximums, without any additional inference burden. We evaluate BGCNet on popular pedestrian detection benchmarks, and achieve the state-of-the-art results, with the significant performance improvement on heavily occluded and small-scale pedestrians.