  Video understanding is an important problem in computer vision. Currently,
the well-studied task in this research is human action recognition, where the
clips are manually trimmed from the long videos, and a single class of human
action is assumed for each clip. However, we may face more complicated
scenarios in the industrial applications. For example, in the real-world urban
pipe 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 anomaly
analysis. In particular, we introduce two high-quality video benchmarks, namely
QV-Pipe and CCTV-Pipe, for anomaly inspection in the real-world urban pipe
systems. Based on these new datasets, we will host two competitions including
(1) Video Defect Classification on QV-Pipe and (2) Temporal Defect Localization
on CCTV-Pipe. In this report, we describe the details of these benchmarks, the
problem definitions of competition tracks, the evaluation metric, and the
result summary. We expect that, this competition would bring new opportunities
and challenges for video understanding in smart city and beyond. The details of
our VideoPipe challenge can be found in https://videopipe.github.io.
