Improving Pedestrian Detection from a Long-tailed Domain Perspective

Although pedestrian detection has developed a lot recently, there still exists some challenging scenarios, such as small-scale, occlusion and low-light. Current works usually focus on one of these scenarios independently and propose specific methods. However, different challenges may happen at a time simultaneously and change across time, making a specific method infeasible in practice. Therefore we are motivated to design a method which is able to handle various challenges and to obtain reasonable performance across different scenarios. In this paper, we first propose Instance Domain Compactness (IDC) to measure the difference of each instance in the feature space and handle hard cases from a novel long-tailed domain perspective. Specifically, we first propose a Feature Augmentation Module (FAM) to augment the tail instances in the feature space, thereby increasing the number and diversity of tail samples. Besides, a IDC-guided loss weighting module (IDCW) is formulated to adaptively re-weight the loss of each sample so as to balance the optimization procedure. Extensive analysis and experiments illustrate that our method improves the generalization of the model without any extra parameters and achieves comparable results across different challenging scenarios on both CityPersons and Caltech datasets.