# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 1 Introduction  
Pedestrian detection is an essential computer vision problem that is widely utilized in many real-world applications, such as autonomous driving systems, robotics, and security monitoring systems. Inspired by deep-learning-based techniques of generic object detection, many research works [1, 2, 3, 4, 5, 6, 7] have achieved high detection accuracy for reasonable scale and non-occluded pedestrians. However, the detection performance is unsatisfactory for the difficult cases, such as crowd scenes, rare pose instances, and poor visibility cases influenced by time of the day or weather. In traffic scenes, pedestrians are likely to be occluded by others or by roadside obstructions. Figure 1 shows several typical occluded cases in which the pedestrians are occluded by other pedestrians, trees, bushes, and cars parked on the roadside. In the Caltech pedestrian dataset [8], only 29% of pedestrians are never occluded, 53% are occluded in some frames, and 19% are occluded in all frames. One can notice that over 70% of pedestrians are occluded in at least one frame. In terms of the occlusion degree, 10% of pedestrians are \"partially\" occluded, and 35% are \"heavily\" occluded. Statistical analysis of the CityPersons dataset [9] indicates a similar situation in which fewer than 30% of pedestrians are not occluded. Since the occluded instances dominate the distribution, detecting pedestrians with occlusion is a critical issue that could considerably affect the overall detection performance. In this paper, we focus on improving the detection performance of occluded pedestrians in traffic scenes, mainly from three aspects: generating geometric transformation-invariant features for the deformed appearance of occludedpedestrians, getting more accurate localization, and improving the test image quality when illumination is poor. Unlike rigid objects, the appearance and shape of pedestrians can be deformed under different poses and occlusions. Most deep neural networks adopt convolutional neural network (CNN) modules. However, the inherent attribute of a CNN unit is sampling the feature map from fixed locations. It is limited because different locations may correspond to pedestrians with different scales and poses. The deformable convolution network (DCN) [10] can adaptively decide the receptive field of the activation unit, and thus we embed it at high-level layers to encode more semantic information. Another important embedded feature is the self-attention module [11] which is designed in our work as an attention block. The attention block captures the dependencies between one position and others and re-weights the feature map with attention guidance. For image data, dependencies are captured by deep stacks of convolutional operations, in which the attention maps are formed within the local receptive fields. Benefiting from dependencies modeled by the attention mechanism, networks can flexibly adjust themselves to improve the representation ability. In our work, we capture the attention feature maps based on the shiftable receptive fields produced by the deformable convolution. The attention block can absorb more context dependency information and guide the network to pay more attention to pedestrian regions while suppressing background regions. Another concern for pedestrian detection in a crowded environment is optimizing the prediction with accurate regression. A well-known evaluation metric is based on intersection over union (IoU), which considers the overlap of areas between two bounding boxes. However, most of the existing methods optimize the prediction by using the \(l_{n}\)-norm loss which computes the distance of two bounding boxes. There is a gap between prediction and evaluation. For instance, the \(l_{2}\)-norm loss of the detection box and the ground truth box are the same in Figure 2a,b, but the IoU values are different. A higher IoU value presents a better localization of predictions which is essential for the detection in crowded scenarios.