The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Single Shot Temporal Action Detection
Open accessAbstract
Temporal action detection is a very important yet challenging problem, since videos in real applications are usually long, untrimmed and contain multiple action instances. This problem requires not only recognizing action categories but also detecting start time and end time of each action instance. Many state-of-the-art methods adopt the "detection by classification" framework: first do proposal, and then classify proposals. The main drawback of this framework is that the boundaries of action instance proposals have been fixed during the classification step. To address this issue, we propose a novel Single Shot Action Detector (SSAD) network based on 1D temporal convolutional layers to skip the proposal generation step via directly detecting action instances in untrimmed video. On pursuit of designing a particular SSAD network that can work effectively for temporal action detection, we empirically search for the best network architecture of SSAD due to lacking existing models that can be directly adopted. Moreover, we investigate into input feature types and fusion strategies to further improve detection accuracy. We conduct extensive experiments on two challenging datasets: THUMOS 2014 and MEXaction2. When setting Intersection-over-Union threshold to 0.5 during evaluation, SSAD significantly outperforms other state-of-the-art systems by increasing mAP from $19.0%$ to $24.6%$ on THUMOS 2014 and from 7.4% to $11.0%$ on MEXaction2.
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Cites
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You Only Look Once: Unified, Real-Time Object Detection
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Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Learning Spatiotemporal Features with 3D Convolutional Networks
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Large-Scale Video Classification with Convolutional Neural Networks
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Hmdb: a large video database for human motion recognition
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Action Recognition with Improved Trajectories
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End-to-End Learning of Action Detection from Frame Glimpses in Videos
Yeung, S., Russakovsky, O., Mori, G., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.293 · Cited by 585
A Multi-stream Bi-directional Recurrent Neural Network for Fine-Grained Action Detection
Singh, B., Marks, T. K., Jones, M., Tuzel, O., & Shao, M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.216 · Cited by 461 · cited 2×
“However, it is di cult for RNNs to keep a long time period memory in practice [32].”
BackgroundMethod
Cited by
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
Lin, T., Liu, X., Li, X., Ding, E., & Wen, S. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00399 · Cited by 694
Self-citationGaussian Temporal Awareness Networks for Action Localization
Long, F., Yao, T., Qiu, Z., Tian, X., Luo, J., & Mei, T. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00043 · Cited by 380
Temporal Deformable Residual Networks for Action Segmentation in Videos
Lei, P., & Todorovic, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00705 · Cited by 202
Alleviating Over-segmentation Errors by Detecting Action Boundaries
Ishikawa, Y., Kasai, S., Aoki, Y., & Kataoka, H. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00237 · Cited by 171
ASFormer: Transformer for Action Segmentation
Yi, F., Wen, H., & Jiang, T. (2021) · Proceedings of the British Machine Vision Conference 2021 · doi:10.5244/c.35.49 · Cited by 170
“The Onestage methods [25, 26] draw on the SSD [27] method in object detection and design endto-end action detection networks with the similar feature pyramid structures.”
MethodCai, Z., Stefanov, K., Dhall, A., & Hayat, M. (2022) · 2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta56598.2022.10034605 · Cited by 99
“into two categories: 2-step approaches which first generate segment proposals and then perform multi-class classification to evaluate the proposals [40], [41], [42] and 1-step approaches which directly generate the final segment predictions [43], [44], [45].”
MethodCai, Z., Ghosh, S., Dhall, A., Gedeon, T., Stefanov, K., & Hayat, M. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103818 · Cited by 63
Metadata
- Authors
- Lin, T., Zhao, X., & Shou, Z.
- Venue
- Proceedings of the 25th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2017-10-19
- Pages
- 988-996
- OpenAlex
- W2766402183
- Cited by
- 385
- References
- 11
- FWCI
- 8.33
- Type
- conference paper
- Language
- en
- Open access
- green
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