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MMA: a multi-view and multi-modality benchmark dataset for human action recognition
- Source :
- Multimedia Tools and Applications. 77:29383-29404
- Publication Year :
- 2018
- Publisher :
- Springer Science and Business Media LLC, 2018.
-
Abstract
- Human action recognition is an active research topic in both computer vision and machine learning communities, which has broad applications including surveillance, biometrics and human computer interaction. In the past decades, although some famous action datasets have been released, there still exist limitations, including the limited action categories and samples, camera views and variety of scenarios. Moreover, most of them are designed for a subset of the learning problems, such as single-view learning problem, cross-view learning problem and multi-task learning problem. In this paper, we introduce a multi-view, multi-modality benchmark dataset for human action recognition (abbreviated to MMA). MMA consists of 7080 action samples from 25 action categories, including 15 single-subject actions and 10 double-subject interactive actions in three views of two different scenarios. Further, we systematically benchmark the state-of-the-art approaches on MMA with respective to all three learning problems by different temporal-spatial feature representations. Experimental results demonstrate that MMA is challenging on all three learning problems due to significant intra-class variations, occlusion issues, views and scene variations, and multiple similar action categories. Meanwhile, we provide the baseline for the evaluation of existing state-of-the-art algorithms.
- Subjects :
- Biometrics
Computer Networks and Communications
business.industry
Computer science
020207 software engineering
02 engineering and technology
Machine learning
computer.software_genre
Multi modality
Variety (cybernetics)
Action (philosophy)
Hardware and Architecture
0202 electrical engineering, electronic engineering, information engineering
Media Technology
Feature (machine learning)
Benchmark (computing)
Action recognition
020201 artificial intelligence & image processing
Artificial intelligence
Baseline (configuration management)
business
computer
Software
Subjects
Details
- ISSN :
- 15737721 and 13807501
- Volume :
- 77
- Database :
- OpenAIRE
- Journal :
- Multimedia Tools and Applications
- Accession number :
- edsair.doi...........a7e3dd3e6c2a07f4723bd809b0c9567b