# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 1 Introduction
The IoU-based regression loss, such as generalized-IoU [12] and distance-IoU [13] instead of \(l_{n}\)-norm loss, can produce more accurate localization of the detection bounding boxes. In addition, the IoU-based non-maximum suppression (NMS), which adaptively changes the threshold with the IoU of the neighboring detection bounding boxes, can refine the occluded detection boxes. Figure 1: Examples of occluded pedestrians in traffic scenes. The green bounding boxes are the ground truth, the red bounding boxes denote the detections of our method. Furthermore, illumination is a critical factor for detection performance. Since photographs are captured under diverse exposure, including overexposure or heavily shaded areas, the illumination frequently influences the visibility of pedestrians in test images. In the Caltech [8] and CityPersons [9] datasets, most of the images are collected in dry weather conditions, while in the EuroCity Persons dataset [14], many of the images were taken in rainy conditions with poor visibility. To cope with the challenging illumination cases, we capitalize a tone mapping technique as a preprocessing step. Our contributions are summarized as follows:  
* First, we have designed a deformable convolution with attention module (DCAM) that generates the attention feature map corresponding to the deformable receptive field. The DCAM enables the network to adapt to diverse poses of pedestrians and occluded instances via deformable convolution. Furthermore, it can obtain attention features to capture effective contextual dependency information among different positions by a non-local (NL) attention block. * Second, we have optimized the detection localization by using an improved loss function. The traditional smooth-L1 loss has been replaced with complete-IoU (CIoU) loss [13] for regression. The regression loss with CIoU, instead of the commonly used \(l_{n}\)-norm, can facilitate prediction with more accurate localization, as shown in Figure 2.
* Third, effective techniques for pedestrian detection in diverse traffic scenes have been explored in our work. The distance IoU-based (DIoU) NMS was adopted to refine the prediction boxes to improve the detection performance of occluded instances. A preprocessing with adaptive local tone mapping (ALTM) based on the Retinex [15] algorithm was implemented to enhance the detection accuracy under poor illuminance. * Finally, experiments on three well-known traffic scene pedestrian benchmarks, Caltech [8], CityPersons [9], and EuroCity Persons (ECP) datasets [14], demonstrated that the proposed method leads to notable improvement in performance for the detection of heavily occluded pedestrians. Compared with the published best results, our proposed method achieved significant improvements of 12.44%, 5.3%, and 5.0%, respectively, in \(MR^{-2}\) of the heavily occluded sets of the Caltech [8], CityPersons [9], and ECP [14] datasets. This paper is organized as follows: Section 2 reviews the existing closely related pedestrian detectors. Section 3 explains the details of our proposed method. Experimental results and ablation studies are presented in Section 4. Finally, conclusions are summarized in Section 5.