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What-Where-When Attention Network for video-based person re-identification
- Source :
- Neurocomputing. 468:33-47
- Publication Year :
- 2022
- Publisher :
- Elsevier BV, 2022.
-
Abstract
- Video-based person re-identification plays a critical role in intelligent video surveillance by learning temporal correlations from consecutive video frames. Most existing methods aim to solve the challenging variations of pose, occlusion, backgrounds and so on by using attention mechanism. They almost all draw attention to the occlusion and learn occlusion-invariant video representations by abandoning the occluded area or frames, while the other areas in these frames contain sufficient spatial information and temporal cues. To overcome these drawbacks, this paper proposes a comprehensive attention mechanism covering what, where, and when to pay attention in the discriminative spatial-temporal feature learning, namely What-Where-When Attention Network (W3AN). Concretely, W3AN designs a spatial attention module to focus on pedestrian identity and obvious attributes by the importance estimating layer (What and Where), and a temporal attention module to calculate the frame-level importance (when), which is embedded into a graph attention network to exploit temporal attention features rather than computing weighted average feature for video frames like existing methods. Moreover, the experiments on three widely-recognized datasets demonstrate the effectiveness of our proposed W3AN model and the discussion of major modules elaborates the contributions of this paper.
- Subjects :
- Focus (computing)
Exploit
business.industry
Computer science
Cognitive Neuroscience
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Machine learning
computer.software_genre
Computer Science Applications
Discriminative model
Artificial Intelligence
Feature (computer vision)
Identity (object-oriented programming)
Graph (abstract data type)
Artificial intelligence
business
Spatial analysis
computer
Feature learning
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 468
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........a49cd19bc9b1261f56c46c300c903dae