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Exploiting inter-frame regional correlation for efficient action recognition
- Source :
- Expert Systems with Applications. 178:114829
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Temporal feature extraction is an important issue in video-based action recognition. Optical flow is a popular method to extract temporal feature, which produces excellent performance thanks to its capacity of capturing pixel-level correlation information between consecutive frames. However, such a pixel-level correlation is extracted at the cost of high computational complexity and large storage resource. In this paper, we propose a novel temporal feature extraction method, named Attentive Correlated Temporal Feature (ACTF), by exploring inter-frame correlation within a certain region. The proposed ACTF exploits both bilinear and linear correlation between successive frames on the regional level. Our method has the advantage of achieving performance comparable to or better than optical flow-based methods while avoiding the introduction of optical flow. Experimental results demonstrate our proposed method achieves the state-of-the-art performances of 96.3% on UCF101 and 76.3% on HMDB51 benchmark datasets.<br />24 pages (exclude reference), 7 figures, 4 tables
- Subjects :
- FOS: Computer and information sciences
0209 industrial biotechnology
Computational complexity theory
Computer science
business.industry
Computer Vision and Pattern Recognition (cs.CV)
Feature extraction
Computer Science - Computer Vision and Pattern Recognition
General Engineering
Optical flow
Inter frame
Bilinear interpolation
Pattern recognition
02 engineering and technology
Computer Science Applications
Correlation
020901 industrial engineering & automation
Artificial Intelligence
Feature (computer vision)
0202 electrical engineering, electronic engineering, information engineering
Benchmark (computing)
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 178
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi.dedup.....f71b353d1670e02aa79d0acfbea61744