- Real-time online system processes, detects and categorizes multiple activities in untrimmed security videos at approximately 100 frames per second
- Directly processes videos using 3D convolutions instead of individual frames
- Applicable to security monitoring and surveillance
The University of Central Florida invention is a real-time online system and method that can detect multiple activities occurring in long, untrimmed security videos. The invention uses a deep learning approach to process videos in an online fashion at a clip level—drastically reducing the computation time in detecting activities. The ability of the method to process one video clip at a time in an online fashion makes it robust against varying length activities. The methodology was tested on the VIRAT and MEVA (Multiview Extended Video with Activities) datasets with more than 250 hours of videos and demonstrated effective performance in terms of processing speed as well as activity detection. The invention can process high-resolution security videos at 100 frames per second.