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Action recognition with trajectory-pooled deep-convolutional descriptors
Open accessAbstract
Visual features are of vital importance for human action understanding in videos. This paper presents a new video representation, called trajectory-pooled deep-convolutional descriptor (TDD), which shares the merits of both hand-crafted features [31] and deep-learned features [24]. Specifically, we utilize deep architectures to learn discriminative convolutional feature maps, and conduct trajectory-constrained pooling to aggregate these convolutional features into effective descriptors. To enhance the robustness of TDDs, we design two normalization methods to transform convolutional feature maps, namely spatiotemporal normalization and channel normalization. The advantages of our features come from (i) TDDs are automatically learned and contain high discriminative capacity compared with those hand-crafted features; (ii) TDDs take account of the intrinsic characteristics of temporal dimension and introduce the strategies of trajectory-constrained sampling and pooling for aggregating deep-learned features. We conduct experiments on two challenging datasets: HMD-B51 and UCF101. Experimental results show that TDDs outperform previous hand-crafted features [31] and deep-learned features [24]. Our method also achieves superior performance to the state of the art on these datasets.
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“Performance of the ED-TCN is on par with the ensemble approach of Singh et al . [28], which combines EgoNet features with TDD [17].”
From Temporal Convolutional Networks for Action Segmentation and Detection (2017) · cites this paper as [17]
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Cited by
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Wang, X., Girshick, R., Gupta, A., & He, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00813 · Cited by 11,596
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Self-citation
Metadata
- Authors
- Wang, L., Qiao, Y., & Tang, X.
- Affiliations
- Chinese University of Hong Kong (HK)
- Chinese Academy of Sciences (CN)
- ORCID
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 4305-4314
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1944615693
- Cited by
- 1,194
- References
- 49
- Citations by year
- 2026: 15 · 2025: 40 · 2024: 47 · 2023: 74 · 2022: 74 · 2021: 111 · 2020: 129 · 2019: 192 · 2018: 202 · 2017: 186 · 2016: 108 · 2015: 16
- FWCI
- 70.00
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Analysis and Summarization
- Keywords
- human action recognition, video representation, channel normalization, handcrafted features
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