The DOI matches its Crossref record.
Recognizing human actions: a local SVM approach
Abstract
Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper, we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.
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
Cited by
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
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117
ActivityNet: A large-scale video benchmark for human activity understanding
Heilbron, F. C., Escorcia, V., Ghanem, B., & Niebles, J. C. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298698 · Cited by 2,578
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
The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
Goyal, R., Kahou, S. E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.622 · Cited by 1,471
Transfer Learning for Visual Categorization: A Survey
Shao, L., Zhu, F., & Li, X. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2330900 · Cited by 895
PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning
Wang, Y., Wu, H., Zhang, J., Gao, Z., Wang, J., Yu, P. S., & Long, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3165153 · Cited by 677
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-citationA 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
Analysis of machine learning techniques for heart failure readmissions
Mortazavi, B. J., Downing, N. S., Bucholz, E. M., Dharmarajan, K., Manhapra, A., Li, S.-X., Negahban, S. N., & Krumholz, H. M. (2016) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.116.003039 · Cited by 401
Understanding egocentric activities
Fathi, A., Farhadi, A., & Rehg, J. M. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126269 · Cited by 384
A survey of depth and inertial sensor fusion for human action recognition
Chen, C., Jafari, R., & Kehtarnavaz, N. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-015-3177-1 · Cited by 356
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
RGBD-HuDaAct: A color-depth video database for human daily activity recognition
Ni, B., Wang, G., & Moulin, P. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130379 · Cited by 304
A Review on Deep Learning Techniques for Video Prediction
Oprea, S., Martinez-Gonzalez, P., Garcia-Garcia, A., Castro-Vargas, J. A., Orts-Escolano, S., Garcia-Rodriguez, J., & Argyros, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3045007 · Cited by 301
Automatic annotation of human actions in video
Duchenne, O., Laptev, I., Sivic, J., Bach, F., & Ponce, J. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459279 · Cited by 275
Self-citationIslam, N., Faheem, Y., Din, I. U., Talha, M., Guizani, M., & Khalil, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.05.059 · Cited by 154
Modeling Actions through State Changes
Fathi, A., & Rehg, J. M. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.333 · Cited by 145
Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning
Lee, S., Kim, H. G., Hwi Choi, D., Kim, H.-I., & Ro, Y. M. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00307 · Cited by 129
Context-Aware Activity Recognition and Anomaly Detection in Video
Zhu, Y., Nayak, N. M., & Roy-Chowdhury, A. K. (2013) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2012.2234722 · Cited by 121
(2016) · MPG.PuRe (Max Planck Society) · Cited by 107
VMRNN: Integrating Vision Mamba and LSTM for Efficient and Accurate Spatiotemporal Forecasting
Tang, Y., Dong, P., Tang, Z., Chu, X., & Liang, J. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00575 · Cited by 70
Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark
Abdel-Salam, R., Mostafa, R., & Hadhood, M. (2021) · Communications in Computer and Information Science · doi:10.1007/978-981-16-0575-8_1 · Cited by 60
Compressive Sequential Learning for Action Similarity Labeling
Qin, J., Liu, L., Zhang, Z., Wang, Y., & Shao, L. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2508600 · Cited by 55
A large-scale multiobjective satellite data transmission scheduling algorithm based on SVM+NSGA-II
Zhang, J., Xing, L., Peng, G., Yao, F., & Chen, C. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.100560 · Cited by 48
Fine-grained activity classification in assembly based on multi-visual modalities
Chen, H., Zendehdel, N., Leu, M. C., & Yin, Z. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-023-02152-x · Cited by 47
N-HAR: A Neuromorphic Event-Based Human Activity Recognition System using Memory Surfaces
Pradhan, B. R., Bethi, Y., Narayanan, S., Chakraborty, A., & Thakur, C. S. (2019) · 2019 IEEE International Symposium on Circuits and Systems (ISCAS) · doi:10.1109/iscas.2019.8702581 · Cited by 31
Metadata
- Authors
- Schuldt, C., Laptev, I., & Caputo, B.
- Venue
- Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.
- Publisher
- IEEE
- Published
- 2004
- Pages
- 32-36 Vol.3
- ISSN
- 1041-3278 (ISSN-L)
- OpenAlex
- W4249279051
- Cited by
- 3,010
- References
- 0
- FWCI
- 10.77
- Type
- conference paper
- Language
- en
- Open access
- closed
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.
