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Neural Processing Letters
Published in: Neural Processing Letters 4/2021

15-05-2021

Boundary Adjusted Network Based on Cosine Similarity for Temporal Action Proposal Generation

Authors: Jingye Zheng, Dihu Chen, Haifeng Hu

Published in: Neural Processing Letters | Issue 4/2021

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Abstract

Detecting temporal actions in long and untrimmed videos is a challenging and important field in computer vision. Generating high-quality proposals is a key step in temporal action detection. A high-quality proposal usually contains two main characteristics. One is the temporal overlaps between proposals and action instances should be as large as possible. The another one is the number of generated proposals should be as few as possible. Inspired by the similarity comparison in face recognition and the similarity of action in same action segment, we design a module to compare the similarity for visual features extracted from visual feature encoder. We find out time points where the similarity of features changes shapely to generate candidate proposals. Then, we train a classifier to evaluate the candidate proposals whether contains or not contains action instances. The experiments suggest that our method outperforms other temporal action proposal generation methods in THUMOS-14 dataset and ActivityNet-v1.3 dataset. In addition, our method still outperforms other methods when using different visual features extracted from different networks.
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Literature
1.
Shou Z, Wang D, Chang SF (2016) Temporal action localization in untrimmed videos via multi-stage cnns. Proceedings of the IEEE conference on computer vision and pattern recognition 1049-1058
2.
Zhao Y, Xiong Y, Wang L, Wu Z, Tang X, Lin D (2017) Temporal action detection with structured segment networks. Proceedings of the IEEE international conference on computer vision 2914-2923
3.
Gao J, Chen K, Nevatia R (2018) Ctap: Complementary temporal action proposal generation. Proceedings of the European conference on computer vision 68-83
4.
Yang K, Qiao P, Li D, et al. (2018) Exploring temporal preservation networks for precise temporal action localization. Thirty-Second AAAI Conference on Artificial Intelligence
5.
Karaman S, Seidenari L, Del Bimbo A (2014) Fast saliency based pooling of fisher encoded dense trajectories. ECCV THUMOS Workshop. 1(2):5
6.
Oneata D, Verbeek J, Schmid C (2014) The lear submission at thumos. 2014: ECCV THUMOS Workshop
7.
Wang L, Qiao Y, Tang X (2014) Action recognition and detection by combining motion and appearance features. THUMOS14 Action Recogn Chall 1(2):2
8.
Lin T, Zhao X, Su H, Wang C, Yang M (2018) Bsn: Boundary sensitive network for temporal action proposal generation. Proceedings of the European conference on computer vision 3-19
9.
Zhou B, Andonian A, Oliva A, Torralba A (2018) Temporal relational reasoning in videos. Proceedings of the European conference on computer vision 803-818
10.
Wu CY, Zaheer M, Hu H, Manmatha R, Smola AJ, Krhenbuhl P (2018) Compressed video action recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 6026-6035
11.
Zhang L, Yang M, Feng X (2011) Sparse representation or collaborative representation: Which helps face recognition?. Proceedings of the IEEE conference on computer vision and pattern recognition 471-478
12.
Zheng J, Yang P, Chen S, Shen G, Wang W (2017) Iterative re-constrained group sparse face recognition with adaptive weights learning. IEEE Trans Image Process 26(5):2408–2423MathSciNetCrossRef
13.
Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. In Advances in neural information processing systems 568-576
14.
Wang H, Schmid C (2013) Action recognition with improved trajectories. Proceedings of the IEEE international conference on computer vision 3551-3558
15.
Wang H, Kläser A, Schmid C, Liu CL (2013) Dense trajectories and motion boundary descriptors for action recognition. Int J Comput Vision 103(1):60–79MathSciNetCrossRef
16.
Wang H, Kl\(\ddot{a}\)ser A, Schmid C, Cheng-Lin L (2011) Action recognition by dense trajectories. Proceedings of the IEEE conference on computer vision and pattern recognition 3169-3176
17.
Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition 1725-1732
18.
Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE conference on computer vision 4489-4497
19.
Buch S, Escorcia V, Ghanem B, Fei-Fei L, Niebles JC (2017) End-to-end, single-stream temporal action detection in untrimmed videos. Proceedings of the British machine vision conference. 1:2
20.
Escorcia V, Heilbron FC, Niebles JC, Ghanem B (2016) Daps: Deep action proposals for action understanding. In European conference on computer vision 768-784
21.
Gao J, Yang Z, Chen K, Sun C, Nevatia R (2017) Turn tap: Temporal unit regression network for temporal action proposals. Proceedings of the IEEE international conference on computer vision 3628-3636
22.
23.
24.
Caba Heilbron F, Carlos Niebles J, Ghanem B (2016) Fast temporal activity proposals for efficient detection of human actions in untrimmed videos. Proceedings of the IEEE conference on computer vision and pattern recognition 1914-1923
25.
Laptev I (2015) On space-time interest points. Int J Comput Vision 64(2–3):107–123
26.
Bodla N, Singh B, Chellappa R, Davis LS (2017) Soft-NMS–Improving Object Detection With One Line of Code. Proceedings of the IEEE conference on computer vision 5561-5569
27.
Caba Heilbron F, Escorcia V, Ghanem B, Carlos Niebles J (2015) Activitynet: A large-scale video benchmark for human activity understanding. Proceedings of the IEEE conference on computer vision and pattern recognition 961-970
28.
Jiang YG, Liu J, Zamir AR, Toderici G, Laptev I, Shah M, Sukthankar R (2014) Thumos challenge: Action recognition with a large number of classes. In: ECCV Workshop
29.
Soomro K, Zamir AR, Shah M (2012) Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:​1212.​0402
30.
Xiong Y, Wang L, Wang Z, Zhang B, Song H, Li W, Lin D, Qiao Y, Gool LV,Tang X (2016) Cuhk & ethz & siat submission to activitynet challenge 2016. arXiv preprint arXiv:​1608.​00797
31.
Lin T, Zhao X, Shou Z (2017) Temporal convolution based action proposal: Submission to activitynet 2017, arXiv:​1707.​06750. [Online]. Available: https://​arxiv.​org/​abs/​1707.​06750
32.
Chao Y-W, Vijayanarasimhan S, Seybold B, Ross DA, Deng J, Sukthankar R (2018) Rethinking the faster R-CNN architecture for temporal action localization, in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1130-1139
33.
Buch S, Escorcia V, Shen C, Ghanem B, Carlos Niebles J (2017) Sst: Single-stream temporal action proposals. Proceedings of the IEEE conference on computer vision and pattern recognition 2911-2920
34.
Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning spatiotemporal features with 3d convolutional networks. Proceedings of the IEEE conference on computer vision 4489-4497
35.
Shou Z, Chan J, Zareian A, Miyazawa K, Chang SF (2017) Cdc: Convolutional-deconvolutional networks for precise temporal action localization in untrimmed videos. arXiv preprint arXiv:​1703.​01515
36.
Xu H, Das A, Saenko K (2017) R-c3d: Region convolutional 3d network for temporal activity detection. In proceedings of the IEEE international conference on computer vision (ICCV) 5783-5792
37.
Metadata
Title
Boundary Adjusted Network Based on Cosine Similarity for Temporal Action Proposal Generation
Authors
Jingye Zheng
Dihu Chen
Haifeng Hu
Publication date
15-05-2021
Publisher
Springer US
Published in
Neural Processing Letters / Issue 4/2021
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10500-2

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