Human action is a high-level concept in computer vision research and understanding it may benefit from different semantics, such as human pose, interacting objects, and scene context. In this paper, we explicitly exploit semantic cues with aid of existing human/object detectors for action recognition in videos, and thoroughly study their effect on the recognition performance for different types of actions. Specifically, we propose a new deep architecture by incorporating human/object detection results into the framework, called two-stream semantic region based CNNs (SR-CNNs). Our proposed architecture not only shares great modeling capacity with the original two-stream CNNs, but also exhibits the flexibility of leveraging semantic cues (e.g. scene, person, object) for action understanding. We perform experiments on the UCF101 dataset and demonstrate its superior performance to the original two-stream CNNs. In addition, we systematically study the effect of incorporating semantic cues on the recognition performance for different types of action classes, and try to provide some insights for building more reasonable action benchmarks and developing better recognition algorithms.
REF incorporate semantic information into the two-stream ConvNets.
Two-Stream SR-CNNs for Action Recognition in Videos
BMVC
Computer Science