# Occluded Pedestrian Detection Techniques by Deformable Attention-Guided Network (DAGN)
## 4 Experimental Results
### Caltech Pedestrian Dataset
#### 4.2.3 Comparison with the State-of-the-Art Methods of Caltech Pedestrian Datasets  
Table 3 shows the detection performances and runtime comparisons with the state-of-the-art methods, including MS-CNN [5], RPN+BF [4], SDS-RCNN [2], ALF [6], CSP [1], and Pedestron [42]. The progressive training pipeline proposed by [PERSON] [42] is a good trick to improve the detection performance further. Since deep-learning-based methods depend heavily on the quantity and quality of data, progressively training the model from a large-scale and diverse training set to the relatively small dataset, which is closer to the target domain, can increase the representation and generalization ability of the model. ECP and CityPersons datasets are denser than the Caltech dataset, in terms of pedestrians per frame and are more diverse in scenarios with higher resolution. CityPersons contains 35,000 manually annotated persons with approximately 7 pedestrians per image on average. EuroCity Persons is nearly one order of magnitude larger with over 238,200 person instances manually labeled, and 9.5 pedestrians in average per image in crowded scenarios. From DAGN to DAGN++, we adopted a similar training strategy to that of Pedestron [42], i.e., pre-training the model from ECP, then fine-tuning from CityPersons.  
Figure 6: (**a**) Comparison of the detection performance based on the baseline method, cascade R-CNN+FPN. Left, without DCAM and right, with DCAM. (**b**) Detection examples with plain NMS (left) and DIoU-NMS (right). Green boxes, ground truth and red boxes, detection results.  
Our proposed DAGN achieved the lowest log-average miss rate of 33.22% on heavy occlusion cases without progressive training strategy, which outperforms the previous best result (38.70%) of SDS-RCNN [2]. For overall performance, our approach achieved 46.83% in \(MR^{-2}\) which exceeds the previous best result (55.77%) of CSP by a significant improvement of 8.94%. One can see that detecting occluded instances is essential for good overall detection performance. After applying the progressive pipeline, the DAGN++ also outperforms the state-of-the-art method, Pedestron, by 12.45% and 7.8% for heavy occlusion and overall performance, respectively, and achieves the competitive miss rate of 1.84%, which is 0.36% inferior to the best result (1.48%) of Pedestron for the reasonable test set.  
Figure 7 presents the detection miss rates versus FPPI with three simulations, namely \"reasonable,\" \"heavy occlusion,\" \"all\", \"overlap--0.75\", and \"overlap--0.85\" of the Caltech testing set. Compared to the standard IoU threshold of 0.5, the overlap ratio between the detected bounding box and the ground truth bounding box should be more than 75% and 85% to be considered as the true positive for \"overlap - 0.75,\" and \"overlap - 0.85\" cases. Our proposed DAGN++ achieved the best results of 7.17% and 32.21% in \(MR^{-2}\) of the \"overlap - 0.75\" and \"overlap - 0.85\" cases, respectively. The results show that our method can produce more accurate localization. Figure 8 shows examples of the detection results of our method compared with the state-of-the-art method, Pedestron [42], of the Caltech pedestrian test set.