The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Evaluation of local spatio-temporal features for action recognition
Open accessAbstract
International audience
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955 · cited 1×
“Like for previous descriptors, the authors divide 3D patches into n x × n y × n t cells, The size of the 3D patch is given by ∆ x ( σ ) = ∆ y ( σ ) = 3 σ , ∆ t ( τ ) = 3 τ .”
A Combined Corner and Edge Detector
Harris, C., & Stephens, M. (1988) · Procedings of the Alvey Vision Conference 1988 · doi:10.5244/c.2.23 · Cited by 12,901
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-citationFeature detection with automatic scale selection
Lindeberg, T. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008045108935 · Cited by 2,541
Cited by
Two-Stream Convolutional Networks for Action Recognition in Videos
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
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
A survey on vision-based human action recognition
Poppe, R. (2010) · Image and Vision Computing · doi:10.1016/j.imavis.2009.11.014 · Cited by 2,242
Dense 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-citationAction recognition with trajectory-pooled deep-convolutional descriptors
Wang, L., Qiao, Y., & Tang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299059 · Cited by 1,194
A Low Power, Fully Event-Based Gesture Recognition System
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Di Nolfo, C., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.781 · Cited by 1,017
Describing Videos by Exploiting Temporal Structure
Yao, L., Torabi, A., Cho, K., Ballas, N., Pal, C., Larochelle, H., & Courville, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.512 · Cited by 1,035
Multiple instance learning: A survey of problem characteristics and applications
Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.009 · Cited by 740
“This agrees with other bag-of-words (BoW) empirical comparisons [50, 51].”
BackgroundThe Language of Actions: Recovering the Syntax and Semantics of Goal-Directed Human Activities
Kuehne, H., Arslan, A., & Serre, T. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.105 · Cited by 571
A database for fine grained activity detection of cooking activities
Rohrbach, M., Amin, S., Andriluka, M., & Schiele, B. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6247801 · Cited by 521
A survey of video datasets for human action and activity recognition
Chaquet, J. M., Carmona, E. J., & Fernández-Caballero, A. (2013) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2013.01.013 · Cited by 455
ModDrop: Adaptive Multi-Modal Gesture Recognition
Neverova, N., Wolf, C., Taylor, G., & Nebout, F. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2461544 · Cited by 420
Combining modality specific deep neural networks for emotion recognition in video
Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, Ç., Memisevic, R., Vincent, P., Courville, A., Bengio, Y., Ferrari, R. C., et al. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531745 · Cited by 391
What Can Help Pedestrian Detection?
Mao, J., Xiao, T., Jiang, Y., & Cao, Z. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.639 · Cited by 306
Slow feature analysis for human action recognition
Zhang, Z., & Tao, D. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.157 · Cited by 307
Super Normal Vector for Human Activity Recognition with Depth Cameras
Yang, X., & Tian, Y. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2565479 · Cited by 162
(2016) · MPG.PuRe (Max Planck Society) · Cited by 107
A Multi-scale Approach to Gesture Detection and Recognition
Neverova, N., Wolf, C., Paci, G., Sommavilla, G., Taylor, G. W., & Nebout, F. (2013) · 2013 IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.69 · Cited by 52
Multi-modal Unsupervised Feature Learning for RGB-D Scene Labeling
Wang, A., Lu, J., Wang, G., Cai, J., & Cham, T.-J. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_30 · Cited by 50
Recognising complex activities with histograms of relative tracklets
Stein, S., & McKenna, S. J. (2017) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2016.08.012 · Cited by 11
Metadata
- Authors
- Wang, H., Ullah, M. M., Klaser, A., Laptev, I., & Schmid, C.
- Venue
- Procedings of the British Machine Vision Conference 2009
- Publisher
- British Machine Vision Association
- Published
- 2009
- Pages
- 124.1-124.11
- OpenAlex
- W1993229407
- Cited by
- 1,296
- References
- 6
- FWCI
- 64.33
- Type
- conference paper
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
