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Video understanding is an important problem in computer vision. Currently,the well-studied task in this research is human action recognition, where theclips are manually trimmed from the long videos, and a single class of humanaction is assumed for each clip. However, we may face more complicatedscenarios in the industrial applications. For example, in the real-world urbanpipe system, anomaly defects are fine-grained, multi-labeled, domain-relevant.To recognize them correctly, we need to understand the detailed video content.For this reason, we propose to advance research areas of video understanding,with a shift from traditional action recognition to industrial anomalyanalysis. In particular, we introduce two high-quality video benchmarks, namelyQV-Pipe and CCTV-Pipe, for anomaly inspection in the real-world urban pipesystems. Based on these new datasets, we will host two competitions including(1) Video Defect Classification on QV-Pipe and (2) Temporal Defect Localizationon CCTV-Pipe. In this report, we describe the details of these benchmarks, theproblem definitions of competition tracks, the evaluation metric, and theresult summary. We expect that, this competition would bring new opportunitiesand challenges for video understanding in smart city and beyond. The details ofour VideoPipe challenge can be found in https://videopipe.github.io.

VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection