Efficient search of Top-K video subvolumes for multi-instance action detection
Action detection was formulated as a subvolume mutual information maximization problem in [8], where each subvolume identifies where and when the action occurs in the video. Despite the fact that the proposed branch-and-bound algorithm can find the best subvolume efficiently for low resolution video...
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| Lenguaje: | Inglés |
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2010
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| LEADER | 06957caa a22007817a 4500 | ||
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| 001 | PAPER-7498 | ||
| 003 | AR-BaUEN | ||
| 005 | 20230518203715.0 | ||
| 008 | 190411s2010 xx ||||fo|||| 10| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-78349239606 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Goussies, N.A. | |
| 245 | 1 | 0 | |a Efficient search of Top-K video subvolumes for multi-instance action detection |
| 260 | |c 2010 | ||
| 270 | 1 | 0 | |m Goussies, N. A.; DC - FCEyN, Univ. de Buenos AiresArgentina; email: ngoussie@dc.uba.ar |
| 506 | |2 openaire |e Política editorial | ||
| 504 | |a Laptev, I., On space-time interest points (2005) International Journal of Computer Vision, 64 (2-3), pp. 107-123 | ||
| 504 | |a Bentley, J., Programming pearls: Algorithm design techniques (1984) Commun. ACM, 27 (9), pp. 865-873 | ||
| 504 | |a Schuldt, C., Laptev, I., Caputo, B., Recognizing human actions: A local svm approach (2004) Proc. IEEE Conf. on Pattern Recognition | ||
| 504 | |a Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B., Learning realistic human actions from movies (2008) Proc. IEEE Conf. on Computer Vision and Pattern Recognition | ||
| 504 | |a Reddy, K.K., Liu, J., Shah, M., Incremental action recognition using feature-tree (2009) Proc. IEEE Intl. Conf. on Computer Vision | ||
| 504 | |a Shechtman, E., Irani, M., Space-time behavior based correlation (2005) Proc. IEEE Conf. on Computer Vision and Pattern Recognition | ||
| 504 | |a Ke, Y., Sukthankar, R., Hebert, M., Event detection in crowded videos (2007) Proc. IEEE International Conf. on Computer Vision | ||
| 504 | |a Yuan, J., Liu, Z., Wu, Y., Discriminative subvolume search for efficient action detection (2009) Proc. IEEE Conf. on Computer Vision and Pattern Recognition | ||
| 504 | |a Hu, Y., Cao, L., Lv, F., Yan, S., Gong, Y., Huang, T.S., Action detection in complex scenes with spatial and temporal ambiguities (2009) Proc. IEEE Intl. Conf. on Computer Vision | ||
| 504 | |a Lampert, C.H., Blaschko, M.B., Hofmann, T., Beyond sliding windows: Object localization by efficient subwindow search (2008) Proc. IEEE Conf. on Computer Vision and Pattern Recognition | ||
| 504 | |a Yuan, J., Liu, Z., Wu, Y., Zhang, Z., Speeding up spatio-temporal sliding-window search for efficient event detection in crowded videos (2009) ACM Multimeida Workshop on Events in Multimedia | ||
| 504 | |a Bobick, A.F., Davis, J.W., The recognition of human movement using temporal templates (2001) IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 23 (3), pp. 257-267 | ||
| 504 | |a Rodriguez, M.D., Ahmed, J., Shah, M., Action mach a spatio-temporal maximum average correlation height filter for action recognition (2008) Proc. IEEE Conf. on Computer Vision and Pattern Recognition | ||
| 504 | |a Weinland, E.B.D., Ronfard, R., Free viewpoint action recognition using motion history volumes (2006) Computer Vision and Image Understanding, 104 (2-3), pp. 207-229 | ||
| 504 | |a Ke, Y., Sukthankar, R., Hebert, M., Efficient visual event detection using volumetric features (2005) Proc. IEEE International Conf. on Computer Vision | ||
| 504 | |a Yang, M., Lv, F., Xu, W., Yu, K., Gong, Y., Human action detection by boosting efficient motion features (2009) IEEE Workshop on Video-oriented Object and Event Classification in Conjunction with ICCV, , Kyoto, Japan, Sept.29-Oct.2 | ||
| 504 | |a Lin, Z., Jiang, Z., Davis, L.S., Recognizing actions by shape-motion prototype trees (2009) Proc. IEEE Intl. Conf. on Computer Vision | ||
| 504 | |a Jiang, H., Drew, M.S., Li, Z.-N., Successive convex matching for action detection (2006) Proc. IEEE Conf. on Computer Vision and Pattern Recognition | ||
| 520 | 3 | |a Action detection was formulated as a subvolume mutual information maximization problem in [8], where each subvolume identifies where and when the action occurs in the video. Despite the fact that the proposed branch-and-bound algorithm can find the best subvolume efficiently for low resolution videos, it is still not efficient enough to perform multiinstance detection in videos of high spatial resolution. In this paper we develop an algorithm that further speeds up the subvolume search and targets on real-time multi-instance action detection for high resolution videos (e.g. 320 × 240 or higher). Unlike the previous branch-and-bound search technique which restarts a new search for each action instance, we find the Top-K subvolumes simultaneously with a single round of search. To handle the larger spatial resolution, we downsample the volume of videos for a more efficient upperbound estimation. To validate our algorithm, we perform experiments on a challenging dataset of 54 video sequences where each video consists of several actions performed by different people in a crowded environment. The experiments show that our method is not only efficient, but also capable of handling action variations caused by performing speed and style changes, spatial scale changes, as well as cluttered and moving background. © 2010 IEEE. |l eng | |
| 593 | |a DC - FCEyN, Univ. de Buenos Aires, Argentina | ||
| 593 | |a Microsoft Research, Redmond, WA, United States | ||
| 593 | |a School of EEE, Nanyang Technological University, Singapore, 39798, Singapore | ||
| 690 | 1 | 0 | |a ACTION RECOGNITION |
| 690 | 1 | 0 | |a BRANCH-AND-BOUND |
| 690 | 1 | 0 | |a ACTION RECOGNITION |
| 690 | 1 | 0 | |a BRANCH AND BOUNDS |
| 690 | 1 | 0 | |a BRANCH-AND-BOUND ALGORITHMS |
| 690 | 1 | 0 | |a DATA SETS |
| 690 | 1 | 0 | |a HIGH RESOLUTION |
| 690 | 1 | 0 | |a HIGH SPATIAL RESOLUTION |
| 690 | 1 | 0 | |a LOW RESOLUTION VIDEO |
| 690 | 1 | 0 | |a MUTUAL INFORMATION MAXIMIZATION |
| 690 | 1 | 0 | |a SEARCH TECHNIQUE |
| 690 | 1 | 0 | |a SPATIAL RESOLUTION |
| 690 | 1 | 0 | |a SPATIAL SCALE |
| 690 | 1 | 0 | |a SUBVOLUMES |
| 690 | 1 | 0 | |a UPPER BOUND |
| 690 | 1 | 0 | |a VIDEO SEQUENCES |
| 690 | 1 | 0 | |a IMAGE RESOLUTION |
| 690 | 1 | 0 | |a VIDEO RECORDING |
| 690 | 1 | 0 | |a ALGORITHMS |
| 700 | 1 | |a Liu, Z. | |
| 700 | 1 | |a Yuan, J. | |
| 711 | 2 | |c Singapore |d 19 July 2010 through 23 July 2010 |g Código de la conferencia: 82118 | |
| 773 | 0 | |d 2010 |h pp. 328-333 |p IEEE Int. Conf. Multimedia Expo, ICME |n 2010 IEEE International Conference on Multimedia and Expo, ICME 2010 |z 9781424474912 |t 2010 IEEE International Conference on Multimedia and Expo, ICME 2010 | |
| 856 | 4 | 1 | |u https://www.scopus.com/inward/record.uri?eid=2-s2.0-78349239606&doi=10.1109%2fICME.2010.5583547&partnerID=40&md5=e453ae4a08d5338ffe4400678a9917e0 |y Registro en Scopus |
| 856 | 4 | 0 | |u https://doi.org/10.1109/ICME.2010.5583547 |y DOI |
| 856 | 4 | 0 | |u https://hdl.handle.net/20.500.12110/paper_97814244_v_n_p328_Goussies |y Handle |
| 856 | 4 | 0 | |u https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_97814244_v_n_p328_Goussies |y Registro en la Biblioteca Digital |
| 961 | |a paper_97814244_v_n_p328_Goussies |b paper |c PE | ||
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| 999 | |c 68451 | ||