Improved two-stream model for human action recognition

Improved Discriminative Model for View- Invariant Human Action Recognition

Recognizing human actions play an important role in applications like video surveillance. The recent past has witnessed an increasing research on view-invariant action recognition. Huang et al. proposed a framework based on discriminative model for human action recognition. This model uses STIP (Space – Time Interest Point) to extract motion features and view invariants. Then a discriminative m...

Encoding Multi-resolution Two-Stream CNNs for Action Recognition

Two-Stream SR-CNNs for Action Recognition in Videos

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...

Two-Stream convolutional nets for action recognition in untrimmed video

We extend the two-stream convolutional net architecture developed by Simonyan for action recognition in untrimmed video clips. The main challenges of this project are first replicating the results of Simonyan et al, and then extending the pipeline to apply it to much longer video clips in which no actions of interest are taking place most of the time. We explore aspects of the performance of th...

Two-Stream Convolutional Networks for Action Recognition in Videos

We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion between frames. We also aim to generalise the best performing hand-crafted features within a data-driven learning framework. Our contribution is three-fold. First, we ...