Activity Detection in Untrimmed Videos

Despite the power of deep architectures, localizing small activities in large video frames with variable durations is still an ongoing challenge. The underlying activities are defined by movements of people and vehicles across video frames, or their interactions with several objects. Major challenges in this domain are scarcity of activities in large videos, detecting and tracking small objects, and identifying types of activities with limited supervision. On the other hand, high performance systems require large amounts of computation and therefore create performance bottlenecks in many hardware platforms, which is another challenge in this domain.


  1. J. Gleason, R. Ranjan, S. Schwarcz, C. D. Castillo, J. C. Chen and R. Chellappa, “A proposal-based solution to spatio-temporal action detection in untrimmed videos”, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 2019.
  2. J. Gleason, C. D. Castillo, R. Chellappa, “Real-time detection of activities in untrimmed videos”, Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), 2020.