An Automated Learning Framework With Limited and Cross-Domain Data for Traffic Equipment Detection From Surveillance Videos

Traffic equipment detection from surveillance videos is of practical significance for temporary traffic element update in high-precision maps. However, there is little relative research developed due to limited labeled data. Based on a detector dubbed Faster R-CNN, we propose an automated learning framework that utilizes easy-to-obtain Internet images containing traffic equipment to acquire the capability of detecting traffic equipment from surveillance videos. In this framework, an appearance weighting module using a comprehensive feature aggregation method is designed to allow Faster R-CNN to converge and generalize quickly by taking limited data (i.e., less than 30 images per class) as input. To further address the cross-domain issue brought by the domain gap between the Internet images and the surveillance video frames, a domain adaptation learning scheme is developed, which aims to align the two domains and guide the framework to learn more robust domain-invariant features. Experimental results show that both the appearance weighting module and the domain adaptation learning scheme could bring a great performance improvement. Moreover, the combination of the two results in a state-of-the-art performance (mAP of 44.6%) even if only 30 training images per class are provided. To sum up, the proposed framework is suitable for traffic equipment detection from surveillance videos and provides an inspiration for other detection tasks with limited and cross-domain data, allowing humans to reduce their efforts and time required for arduous data collection and annotation.