Name
Abstract | ||
|---|---|---|
Recognizing human actions from the video streams has become one of the very popular research areas in computer vision and deep learning in the recent years. Action recognition is wildly used in different scenarios in real life, such as surveillance, robotics, healthcare, video indexing and human-computer interaction. The challenges and complexity involved in developing a video-based human action recognition system are manifold. In particular, recognizing actions with similar gestures and describing complex actions is a very challenging problem. To address these issues, we study the problem of classifying human actions using Convolutional Neural Networks (CNN) and develop a hierarchical 3DCNN architecture for similar gesture recognition. The proposed model firstly combines similar gesture pairs into one class, and classify them along with all other class, as a stage-1 classification. In stage-2, similar gesture pairs are classified individually, which reduces the problem to binary classification. We apply and evaluate the developed models to recognize the similar human actions on the HMDB51 dataset. The result shows that the proposed model can achieve high performance in comparison to the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/DICTA.2018.8615804 | 2018 Digital Image Computing: Techniques and Applications (DICTA) |
Keywords | Field | DocType |
Action Recognition,Neural Networks,Deep Learning,Computer Vision | Pattern recognition,Binary classification,Computer science,Convolutional neural network,Gesture,Search engine indexing,Gesture recognition,Artificial intelligence,Deep learning,Artificial neural network,Robotics | Conference |
ISBN | Citations | PageRank |
978-1-5386-6603-6 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Di Wu | 1 | 636 | 117.73 |
| Nabin Sharma | 2 | 132 | 11.55 |
| M. Blumenstein | 3 | 168 | 31.87 |