Revisiting Evaluation of Deep Neural Networks for Pedestrian Detection

The reliable DNN-based perception of pedestrians represents a crucial step towards automated driving systems. Currently applied metrics for a subset-based evaluation prohibit an application-oriented performance evaluation of DNNs for pedestrian detection. We argue that the current limitation in evaluation can be mitigated by the use of image segmentation. In this work, we leverage the instance and semantic segmentation of Cityscapes to describe a rule-based categorization of potential detection errors for CityPersons. Based on our systematic categorization, the filtered log-average miss rate as a new performance metric for pedestrian detection is introduced. Additionally, we derive and analyze a meaningful upper bound for the confidence threshold. We train and evaluate four backbones as part of a generic pedestrian detector and achieve state-of-the-art performance on CityPersons by using a rather simple architecture. Our results and comprehensible analysis show benefits of the newly proposed performance metrics.