Video Activity Recognition With Varying Rhythms

Recognizing normal and anomalous events in long and complex videos with multiple sub-activities has received considerable attention in recent years. This task is more challenging than traditional action recognition in short and relatively homogeneous video clips. Other than the difficulty in recognizing activities in long videos, one other challenge is the varying activity rhythms. The rhythm of sub-actions in an activity can differ in nature and can pose additional challenges that affect the performance of activity recognition methods. In this article, five video activity recognition methods were evaluated using two publicly available video datasets, Breakfast and VIRAT, which consist of long and complex videos. Extensive experiments and analyses showed that among these methods, VideoGraph, was found to perform distinctly better than the other investigated methods while maintaining high accuracy even if the test videos were exposed to severe rhythm changes. The results indicated that VideoGraph is less sensitive to varying rhythms in contrast to other investigated methods. By changing some of the architecture parameters, we also observed performance improvements in VideoGraph.