  Activity detection is one of the attractive computer vision tasks to exploit
the video streams captured by widely installed cameras. Although achieving
impressive performance, conventional activity detection algorithms are usually
designed under certain constraints, such as using trimmed and/or
object-centered video clips as inputs. Therefore, they failed to deal with the
multi-scale multi-instance cases in real-world unconstrained video streams,
which are untrimmed and have large field-of-views. Real-time requirements for
streaming analysis also mark brute force expansion of them unfeasible.
  To overcome these issues, we propose Argus++, a robust real-time activity
detection system for analyzing unconstrained video streams. The design of
Argus++ introduces overlapping spatio-temporal cubes as an intermediate concept
of activity proposals to ensure coverage and completeness of activity detection
through over-sampling. The overall system is optimized for real-time processing
on standalone consumer-level hardware. Extensive experiments on different
surveillance and driving scenarios demonstrated its superior performance in a
series of activity detection benchmarks, including CVPR ActivityNet ActEV 2021,
NIST ActEV SDL UF/KF, TRECVID ActEV 2020/2021, and ICCV ROAD 2021.
