UrbanPipe Challenge on Fine-grained Video Anomaly Recognition

Track Organizers: Xuan Zhang, Yi Liu, Ying Li, Guixin Liang, Fei Xie, Wei Yao, Yi Dai, Yali Wang, Yu Qiao

Video anomaly analysis is important for industrial applications in the real world. In particular, the urban pipe system is one of the most important infrastructures in a city. In order to ensure its normal operation, we need to inspect pipe defects smartly.

For video anomaly recognition, we focus on fine-grained and multi-labeled defect recognition in the real-world urban systems. We have collected a new dataset, termed as UrbanPipe. UrbanPipe is collected from various Quick-View (QV) Inspection devices in the real-world urban pipe systems. It consists of 9.6k short videos with 16 anomaly classes. The total duration of all videos exceeds 55 hours. Given a QV video, our goal is to predict multiple labels of pipe defects in this video.

Evaluation Metric: Since each video contains multiple defect categories, we use Average Precision (AP) to evaluate the recognition results on each defect category. Then we average AP over all the categories to obtain mAP.

Please refer to the competition page for more information.