The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Action and Event Recognition with Fisher Vectors on a Compact Feature Set
Open accessAbstract
Action recognition in uncontrolled video is an important and challenging computer vision problem. Recent progress in this area is due to new local features and models that capture spatio-temporal structure between local features, or human-object interactions. Instead of working towards more complex models, we focus on the low-level features and their encoding. We evaluate the use of Fisher vectors as an alternative to bag-of-word histograms to aggregate a small set of state-of-the-art low-level descriptors, in combination with linear classifiers. We present a large and varied set of evaluations, considering (i) classification of short actions in five datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that for basic action recognition and localization MBH features alone are enough for state-of-the-art performance. For complex events we find that SIFT and MFCC features provide complementary cues. On all three problems we obtain state-of-the-art results, while using fewer features and less complex models.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916
Self-citationVideo Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
Self-citationDense Trajectories and Motion Boundary Descriptors for Action Recognition
Wang, H., Kläser, A., Schmid, C., & Liu, C.-L. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-012-0594-8 · Cited by 1,669
Self-citationImage Classification with the Fisher Vector: Theory and Practice
Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498
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Cited by
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
Self-citationTemporal Action Detection with Structured Segment Networks
Zhao, Y., Xiong, Y., Wang, L., Wu, Z., Tang, X., & Lin, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.317 · Cited by 892
P-CNN: Pose-Based CNN Features for Action Recognition
Cheron, G., Laptev, I., & Schmid, C. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.368 · Cited by 633
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
Attention-Based 3D-CNNs for Large-Vocabulary Sign Language Recognition
Huang, J., Zhou, W., Li, H., & Li, W. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2870740 · Cited by 232
Metadata
- Authors
- Oneata, D., Verbeek, J., & Schmid, C.
- Venue
- 2013 IEEE International Conference on Computer Vision
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 1817-1824
- OpenAlex
- W2131042978
- Cited by
- 376
- References
- 7
- FWCI
- 39.68
- Type
- conference paper
- Language
- en
- Open access
- green
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- Oneata, D., Verbeek, J., & Schmid, C. (2013). Action and Event Recognition with Fisher Vectors on a Compact Feature Set. 2013 IEEE International Conference on Computer Vision. https://doi.org/10.1109/iccv.2013.228
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