Bibliography

Major publications by the team in recent years

• 1A. Avanzi, F. Brémond, C. Tornieri, M. Thonnat.
  Design and Assessment of an Intelligent Activity Monitoring Platform, in: EURASIP Journal on Applied Signal Processing, Special Issue on “Advances in Intelligent Vision Systems: Methods and Applications”, August 2005, vol. 2005:14, pp. 2359-2374.
• 2H. Benhadda, J. Patino, E. Corvee, F. Bremond, M. Thonnat.
  Data Mining on Large Video Recordings, in: 5eme Colloque Veille Stratégique Scientifique et Technologique VSST 2007, Marrakech, Marrocco, 21st - 25th October 2007.
• 3B. Boulay, F. Bremond, M. Thonnat.
  Applying 3D Human Model in a Posture Recognition System, in: Pattern Recognition Letter, 2006, vol. 27, n^o 15, pp. 1785-1796.
• 4F. Brémond, M. Thonnat.
  Issues of Representing Context Illustrated by Video-surveillance Applications, in: International Journal of Human-Computer Studies, Special Issue on Context, 1998, vol. 48, pp. 375-391.
• 5G. Charpiat.
  Learning Shape Metrics based on Deformations and Transport, in: Proceedings of ICCV 2009 and its Workshops, Second Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA), Kyoto, Japan, September 2009.
• 6N. Chleq, F. Brémond, M. Thonnat.
  Advanced Video-based Surveillance Systems, Kluwer A.P. , Hangham, MA, USA, November 1998, pp. 108-118.
• 7F. Cupillard, F. Brémond, M. Thonnat.
  Tracking Group of People for Video Surveillance, Video-Based Surveillance Systems, Kluwer Academic Publishers, 2002, vol. The Kluwer International Series in Computer Vision and Distributed Processing, pp. 89-100.
• 8F. Fusier, V. Valentin, F. Bremond, M. Thonnat, M. Borg, D. Thirde, J. Ferryman.
  Video Understanding for Complex Activity Recognition, in: Machine Vision and Applications Journal, 2007, vol. 18, pp. 167-188.
• 9B. Georis, F. Bremond, M. Thonnat.
  Real-Time Control of Video Surveillance Systems with Program Supervision Techniques, in: Machine Vision and Applications Journal, 2007, vol. 18, pp. 189-205.
• 10C. Liu, P. Chung, Y. Chung, M. Thonnat.
  Understanding of Human Behaviors from Videos in Nursing Care Monitoring Systems, in: Journal of High Speed Networks, 2007, vol. 16, pp. 91-103.
• 11N. Maillot, M. Thonnat, A. Boucher.
  Towards Ontology Based Cognitive Vision, in: Machine Vision and Applications (MVA), December 2004, vol. 16, n^o 1, pp. 33-40.
• 12V. Martin, J.-M. Travere, F. Bremond, V. Moncada, G. Dunand.
  Thermal Event Recognition Applied to Protection of Tokamak Plasma-Facing Components, in: IEEE Transactions on Instrumentation and Measurement, Apr 2010, vol. 59, n^o 5, pp. 1182-1191.
• 13S. Moisan.
  Knowledge Representation for Program Reuse, in: European Conference on Artificial Intelligence (ECAI), Lyon, France, July 2002, pp. 240-244.
• 14S. Moisan.
  Une plate-forme pour une programmation par composants de systèmes à base de connaissances, Université de Nice-Sophia Antipolis, April 1998, Habilitation à diriger les recherches.
• 15S. Moisan, A. Ressouche, J.-P. Rigault.
  Blocks, a Component Framework with Checking Facilities for Knowledge-Based Systems, in: Informatica, Special Issue on Component Based Software Development, November 2001, vol. 25, n^o 4, pp. 501-507.
• 16J. Patino, H. Benhadda, E. Corvee, F. Bremond, M. Thonnat.
  Video-Data Modelling and Discovery, in: 4th IET International Conference on Visual Information Engineering VIE 2007, London, UK, 25th - 27th July 2007.
• 17J. Patino, E. Corvee, F. Bremond, M. Thonnat.
  Management of Large Video Recordings, in: 2nd International Conference on Ambient Intelligence Developments AmI.d 2007, Sophia Antipolis, France, 17th - 19th September 2007.
• 18A. Ressouche, D. Gaffé, V. Roy.
  Modular Compilation of a Synchronous Language, in: Software Engineering Research, Management and Applications, R. Lee (editor), Studies in Computational Intelligence, Springer, 2008, vol. 150, pp. 157-171, selected as one of the 17 best papers of SERA'08 conference.
• 19A. Ressouche, D. Gaffé.
  Compilation Modulaire d'un Langage Synchrone, in: Revue des sciences et technologies de l'information, série Théorie et Science Informat ique, June 2011, vol. 4, n^o 30, pp. 441-471.
  http://hal.inria.fr/inria-00524499/en
• 20M. Thonnat, S. Moisan.
  What Can Program Supervision Do for Software Re-use?, in: IEE Proceedings - Software Special Issue on Knowledge Modelling for Software Components Reuse, 2000, vol. 147, n^o 5.
• 21M. Thonnat.
  Vers une vision cognitive: mise en oeuvre de connaissances et de raisonnements pour l'analyse et l'interprétation d'images, Université de Nice-Sophia Antipolis, October 2003, Habilitation à diriger les recherches.
• 22M. Thonnat.
  Special issue on Intelligent Vision Systems, in: Computer Vision and Image Understanding, May 2010, vol. 114, n^o 5, pp. 501-502.
• 23A. Toshev, F. Brémond, M. Thonnat.
  An A priori-based Method for Frequent Composite Event Discovery in Videos, in: Proceedings of 2006 IEEE International Conference on Computer Vision Systems, New York USA, January 2006.
• 24V. Vu, F. Brémond, M. Thonnat.
  Temporal Constraints for Video Interpretation, in: Proc of the 15th European Conference on Artificial Intelligence, Lyon, France, 2002.
• 25V. Vu, F. Brémond, M. Thonnat.
  Automatic Video Interpretation: A Novel Algorithm based for Temporal Scenario Recognition, in: The Eighteenth International Joint Conference on Artificial Intelligence (IJCAI'03), 9-15 September 2003.
• 26N. Zouba, F. Bremond, A. Anfosso, M. Thonnat, E. Pascual, O. Guerin.
  Monitoring elderly activities at home, in: Gerontechnology, May 2010, vol. 9, n^o 2.

Publications of the year

Articles in International Peer-Reviewed Journals

• 27C. F. Crispim-Junior, V. Buso, K. Avgerinakis, G. Meditskos, A. Briassouli, J. Benois-Pineau, Y. KOMPATSIARIS, F. Bremond.
  Semantic Event Fusion of Different Visual Modality Concepts for Activity Recognition, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, vol. 38, pp. 1598 - 1611. [ DOI : 10.1109/TPAMI.2016.2537323 ]
  https://hal.inria.fr/hal-01399025
• 28A. Dantcheva, F. Brémond.
  Gender estimation based on smile-dynamics, in: IEEE Transactions on Information Forensics and Security, 2016, 11 p. [ DOI : 10.1109/TIFS.2016.2632070 ]
  https://hal.archives-ouvertes.fr/hal-01412408

Invited Conferences

• 29S. Chen, F. Bremond, H. Nguyen, H. Thomas.
  Exploring Depth Information for Head Detection with Depth Images, in: AVSS 2016 - 13th International Conference on Advanced Video and Signal-Based Surveillance, Colorado Springs, United States, August 2016.
  https://hal.inria.fr/hal-01414757
• 30F. F. Negin, S. Cosar, M. F. Koperski, C. F. Crispim-Junior, K. Avgerinakis, F. F. Bremond.
  A hybrid framework for online recognition of activities of daily living in real-world settings, in: 13th IEEE International Conference on Advanced Video and Signal Based Surveillance - AVSS 2016, Colorado springs, United States, IEEE, August 2016. [ DOI : 10.1109/AVSS.2016.7738021 ]
  https://hal.inria.fr/hal-01384710

International Conferences with Proceedings

• 31C. F. Crispim-Junior, M. Koperski, S. Cosar, F. Bremond.
  Semi-supervised understanding of complex activities from temporal concepts, in: 13th International Conference on Advanced Video and Signal-Based Surveillance, Colorado Springs, United States, August 2016.
  https://hal.inria.fr/hal-01398958
• 32E. De Maria, A. Muzy, D. Gaffé, A. Ressouche, F. Grammont.
  Verification of Temporal Properties of Neuronal Archetypes Modeled as Synchronous Reactive Systems, in: HSB 2016 - 5th International Workshop Hybrid Systems Biology, Grenoble, France, Lecture Notes in Bioinformatics series, October 2016, 15 p. [ DOI : 10.1007/978-3-319-47151-8_7 ]
  https://hal.inria.fr/hal-01377288
• 33F. M. Khan, F. Bremond.
  Unsupervised data association for metric learning in the context of multi-shot person re-identification, in: Advance Video and Signal based Surveillance, Colorado Springs, United States, August 2016. [ DOI : 10.1109/AVSS.2016.7738058 ]
  https://hal.inria.fr/hal-01400147
• 34F. Negin, J. Bourgeois, E. Chapoulie, P. Robert, F. Bremond.
  Praxis and Gesture Recognition, in: The 10th World Conference of Gerontechnology (ISG 2016), Nice, France, September 2016.
  https://hal.inria.fr/hal-01416372

Conferences without Proceedings

• 35P. Bilinski, A. Dantcheva, F. Brémond.
  Can a smile reveal your gender?, in: 15th International Conference of the Biometrics Special Interest Group (BIOSIG 2016), Darmstadt, Germany, September 2016.
  https://hal.archives-ouvertes.fr/hal-01387134
• 36E. Gonzalez-Sosa, A. Dantcheva, R. Vera-Rodriguez, J.-L. Dugelay, F. Brémond, J. Fierrez.
  Image-based Gender Estimation from Body and Face across Distances, in: 23rd International Conference on Pattern Recognition (ICPR 2016): "Image analysis and machine learning for scene understanding", Cancun, Mexico, December 2016.
  https://hal.archives-ouvertes.fr/hal-01384324
• 37M. Koperski, F. Bremond.
  Modeling Spatial Layout of Features for Real World Scenario RGB-D Action Recognition, in: AVSS 2016, Colorado Springs, United States, August 2016, pp. 44 - 50. [ DOI : 10.1109/AVSS.2016.7738023 ]
  https://hal.inria.fr/hal-01399037
• 38M. K. Phan Tran, P. Robert, F. Bremond.
  A Virtual Agent for enhancing performance and engagement of older people with dementia in Serious Games, in: Workshop Artificial Compagnon-Affect-Interaction 2016, Brest, France, June 2016.
  https://hal.archives-ouvertes.fr/hal-01369878
• 39N. Thi Lan Anh, F. Bremond, J. Trojanova.
  Multi-Object Tracking of Pedestrian Driven by Context, in: Advance Video and Signal-based Surveillance, Colorado Springs, United States, IEEE, August 2016.
  https://hal.inria.fr/hal-01383186

Internal Reports

• 40E. De Maria, A. Muzy, D. Gaffé, A. Ressouche, F. Grammont.
  Verification of Temporal Properties of Neuronal Archetypes Using Synchronous Models, UCA, Inria ; UCA, I3S ; UCA, LEAT ; UCA, LJAD, July 2016, n^o RR-8937, 21 p.
  https://hal.inria.fr/hal-01349019
• 41S. Moisan, J.-P. Rigault.
  Dynamic Reconfiguration of Feature Models: an Algorithm and its Evaluation, Inria Sophia Antipolis, November 2016, n^o RR-8972, 16 p.
  https://hal.inria.fr/hal-01392796

Other Publications

• 42C. F. Crispim-Junior, A. Konig, R. David, P. Robert, F. Bremond.
  Automatic prediction of autonomy in activities of daily living of older adults, November 2016, 74s p, Short-paper.
  https://hal.inria.fr/hal-01399259
• 43F. M. Khan, F. M. Brémond.
  Person Re-identification for Real-world Surveillance Systems, November 2016, working paper or preprint.
  https://hal.inria.fr/hal-01399939

References in notes

• 44M. Acher, P. Collet, F. Fleurey, P. Lahire, S. Moisan, J.-P. Rigault.
  Modeling Context and Dynamic Adaptations with Feature Models, in: Models@run.time Workshop, Denver, CO, USA, October 2009.
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• 45M. Acher, P. Lahire, S. Moisan, J.-P. Rigault.
  Tackling High Variability in Video Surveillance Systems through a Model Transformation Approach, in: ICSE'2009 - MISE Workshop, Vancouver, Canada, May 2009.
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• 46A. Alahi, L. Jacques, Y. Boursier, P. Vandergheynst.
  Sparsity-driven people localization algorithm: Evaluation in crowded scenes environments, in: PETS workshop, 2009.
• 47A. Andriyenko, K. Schindler.
  Multi-target tracking by continuous energy minimization, in: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, June 2011, pp. 1265-1272. [ DOI : 10.1109/CVPR.2011.5995311 ]
• 48D. Arsic, A. Lyutskanov, G. Rigoll, B. Kwolek.
  Multi-camera person tracking applying a graph-cuts based foreground segmentation in a homography framework, in: PETS workshop, 2009.
• 49K. Avgerinakis, A. Briassouli, I. Kompatsiaris.
  Activity detection using sequential statistical boundary detection (ssbd), in: to appear in Computer Vision and Image Understanding, CVIU, 2015.
• 50S.-H. Bae, K.-J. Yoon.
  Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning, in: CVPR, Columbus, IEEE, June 2014.
• 51A. Bar-Hillel, D. Levi, E. Krupka, C. Goldberg.
  Part-based feature synthesis for human detection, in: ECCV, 2010.
• 52H. Ben Shitrit, J. Berclaz, F. Fleuret, P. Fua.
  Tracking multiple people under global appearance constraints, in: IEEE International Conference on Computer Vision (ICCV), 2011, pp. 137-144.
• 53R. Benenson, M. Mathias, R. Timofte, L. V. Gool.
  Pedestrian detection at 100 frames per second, in: CVPR, 2013.
• 54B. Berkin, B. K. Horn, I. Masaki.
  Fast Human Detection With Cascaded Ensembles On The GPU, in: IEEE Intelligent Vehicles Symposium, 2010.
• 55M. D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier, L. van Gool.
  Markovian tracking-by-detection from a single, uncalibrated camera, in: PETS workshop, 2009, pp. 71-78.
• 56D. P. Chau, J. Badie, F. Bremond, M. Thonnat.
  Online Tracking Parameter Adaptation based on Evaluation, in: IEEE International Conference on Advanced Video and Signal-based Surveillance, Krakow, Poland, August 2013.
  https://hal.inria.fr/hal-00846920
• 57D. P. Chau, F. Bremond, M. Thonnat.
  Online evaluation of tracking algorithm performance, in: The 3rd International Conference on Imaging for Crime Detection and Prevention (ICDP), London,UK, , December 2009.
  https://hal.inria.fr/inria-00486479
• 58D. P. Chau, M. Thonnat, F. Bremond, E. Corvee.
  Online Parameter Tuning for Object Tracking Algorithms, in: Image and Vision Computing, February 2014, vol. 32, n^o 4, pp. 287-302.
  https://hal.inria.fr/hal-00976594
• 59D. Conte, P. Foggia, G. Percannella, M. Vento.
  Performance evaluation of a people tracking system on the pets video database, in: PETS workshop, 2009.
• 60A. D. Costea, S. Nedevschi.
  Word Channel Based Multiscale Pedestrian Detection without Image Resizing and Using Only One Classifier, in: CVPR, 2014.
• 61N. Dalal, B. Triggs.
  Histograms of oriented gradients for human detection, in: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, IEEE, 2005, vol. 1, pp. 886–893.
• 62N. Dalal, B. Triggs, C. Schmid.
  Human detection using oriented histograms of flow and appearance, in: European conference on computer vision, Springer, 2006, pp. 428–441.
• 63R. David, E. Mulin, P. Mallea, P. Robert.
  Measurement of Neuropsychiatric Symptoms in Clinical Trials Targeting Alzheimer's Disease and Related Disorders, in: Pharmaceuticals, 2010, vol. 3, pp. 2387-2397.
• 64P. Dollar, S. Belongie, P. Perona.
  The Fastest Pedestrian Detector in the West, in: BMVC, 2010.
• 65P. Dollar, Z. Tu, P. Perona, S. Belongie.
  Integral channel features, in: BMVC, 2009.
• 66S. Elloumi, S. Cosar, G. Pusiol, F. Bremond, M. Thonnat.
  Unsupervised discovery of human activities from long-time videos, in: IET Computer Vision, March 2015, 1 p.
  https://hal.inria.fr/hal-01123895
• 67P. Felzenszwalb, R. Girshick, D. McAllester, D. Ramanan.
  Object Detection with Discriminatively Trained Part-Based Models, in: PAMI, 2009, vol. 32, n^o 9, pp. 1627–1645.
• 68P. Felzenszwalb, D. McAllester, D. Ramanan.
  A discriminatively trained, multiscale, deformable part model, in: CVPR, 2008.
• 69J. Ferryman, A. Shahrokni.
  An overview of the pets2009 challenge, in: PETS, 2009.
• 70Q. Gao, S. Sun.
  Trajectory-based human activity recognition with hierarchical Dirichlet process hidden Markov models, in: Proceedings of the 1st IEEE China Summit and International Conference on Signal and Information Processing, 2013.
• 71W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan, S. Maybank.
  A system for learning statistical motion patterns, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, vol. 28, n^o 9, pp. 1450–1464.
• 72J.-F. Hu, W.-S. Zheng, J. Lai, J. Zhang.
  Jointly learning heterogeneous features for RGB-D activity recognition, in: CVPR, 2015. [ DOI : 10.1109/CVPR.2015.7299172 ]
• 73A. Karakostas, A. Briassouli, K. Avgerinakis, I. Kompatsiaris, M. Tsolaki.
  The Dem@Care Experiments and Datasets: a Technical Report, 2014.
• 74Y. Kong, Y. Fu.
  Bilinear heterogeneous information machine for RGB-D action recognition, in: CVPR, 2015.
• 75H. S. Koppula, R. Gupta, A. Saxena.
  Learning Human Activities and Object Affordances from RGB-D Videos, in: Int. J. Rob. Res., July 2013, vol. 32, n^o 8, pp. 951–970.
  http://dx.doi.org/10.1177/0278364913478446
• 76H. Koppula, A. Saxena.
  Learning spatio-temporal structure from rgb-d videos for human activity detection and anticipation, in: ICML, 2013.
• 77C. Kästner, S. Apel, S. Trujillo, M. Kuhlemann, D. Batory.
  Guaranteeing Syntactic Correctness for All Product Line Variants: A Language-Independent Approach, in: TOOLS (47), 2009, pp. 175-194.
• 78W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed.
  SSD: Single Shot MultiBox Detector, in: arXiv preprint, 2015, pp. 1–15. [ DOI : 10.1016/j.nima.2015.05.028 ]
  http://arxiv.org/abs/1512.02325
• 79L. Liu, L. Shao.
  Learning Discriminative Representations from RGB-D Video Data, in: IJCAI, 2013.
• 80C. Lu, J. Jia, C.-K. Tang.
  Range-Sample Depth Feature for Action Recognition, in: CVPR, 2014.
• 81A. Milan, K. Schindler, S. Roth.
  Multi-Target Tracking by Discrete-Continuous Energy Minimization, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, vol. PP, n^o 99, pp. 1-1. [ DOI : 10.1109/TPAMI.2015.2505309 ]
• 82S. Moisan, J.-P. Rigault, M. Acher, P. Collet, P. Lahire.
  Run Time Adaptation of Video-Surveillance Systems: A software Modeling Approach, in: ICVS, 8th International Conference on Computer Vision Systems, Sophia Antipolis, France, September 2011.
  http://hal.inria.fr/inria-00617279/en
• 83B. Morris, M. Trivedi.
  Trajectory Learning for Activity Understanding: Unsupervised, Multilevel, and Long-Term Adaptive Approach, in: IEEE Transactions on Pattern Analysis and Machine Intelligence, Nov 2011, vol. 33, n^o 11, pp. 2287-2301. [ DOI : 10.1109/TPAMI.2011.64 ]
• 84M. Mozaz, M. Garaigordobil, L. J. G. Rothi, J. Anderson, G. P. Crucian, K. M. Heilman.
  Posture recognition in Alzheimer’s disease, in: Brain and cognition, 2006, vol. 62, n^o 3, pp. 241–245.
• 85T. L. A. NGUYEN, D. P. CHAU, F. Bremond.
  Robust Global Tracker based on an Online Estimation of Tracklet Descriptor Reliability, in: Advanded Video and Signal-based Surveillance, Karlsruhe, Germany, August 2015.
  https://hal.inria.fr/hal-01185874
• 86B. Ni, P. Moulin, S. Yan.
  Order-Preserving Sparse Coding for Sequence Classification, in: ECCV, 2012.
• 87O. Oreifej, Z. Liu.
  HON4D: Histogram of oriented 4D normals for activity recognition from depth sequences, in: CVPR, 2013.
• 88S. Ren, K. He, R. Girshick, J. Sun.
  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, 2015.
  https://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf
• 89L. M. Rocha, S. Moisan, J.-P. Rigault, S. Sagar.
  Girgit: A Dynamically Adaptive Vision System for Scene Understanding, in: ICVS, Sophia Antipolis, France, September 2011.
  http://hal.inria.fr/inria-00616642/en
• 90M. Rohrbach, M. Regneri, M. Andriluka, S. Amin, M. Pinkal, B. Schiele.
  Script Data for Attribute-Based Recognition of Composite Activities, in: Computer Vision - ECCV 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I, 2012, pp. 144–157.
  http://dx.doi.org/10.1007/978-3-642-33718-5_11
• 91R. Romdhane, E. Mulin, A. Derreumeaux, N. Zouba, J. Piano, L. Lee, I. Leroi, P. Mallea, R. David, M. Thonnat, F. Bremond, P. Robert.
  Automatic Video Monitoring system for assessment of Alzheimer's Disease symptoms, in: The Journal of Nutrition, Health and Aging Ms(JNHA), 2011, vol. JNHA-D-11-00004R1.
  http://hal.inria.fr/inria-00616747/en
• 92L. Rybok, B. Schauerte, Z. Al-Halah, R. Stiefelhagen.
  Important stuff, everywhere! Activity recognition with salient proto-objects as context, in: WACV, 2014.
• 93I. Sarray, A. Ressouche, D. Gaffé, J.-Y. Tigli, S. Lavirotte.
  Safe Composition in Middleware for the Internet of Things, in: Middleware for Context-aware Applications for Internet of thing (M4IoT), Vancouver, Canada, December 2015. [ DOI : 10.1145/2836127.2836131 ]
  https://hal.inria.fr/hal-01236976
• 94L. Seidenari, V. Varano, S. Berretti, A. Del Bimbo, P. Pala.
  Recognizing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses, in: CVPRW, 2013.
• 95A. Shahroudy, G. Wang, T.-T. Ng.
  Multi-modal feature fusion for action recognition in RGB-D sequences, in: ISCCSP, 2014.
• 96S. Tang, M. Andriluka, A. Milan, K. Schindler, S. Roth, B. Schiele.
  Learning People Detectors for Tracking in Crowded Scenes, in: IEEE International Conference on Computer Vision (ICCV), December 2013.
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• 97S. Walk, N. Majer, K. Schindler, B. Schiele.
  New features and insights for pedestrian detection, in: CVPR, 2010.
• 98X. Wang, T. X. Han, S. Yan.
  An hog-lbp human detector with partial occlusion handling, in: ICCV, 2009.
• 99H. Wang, A. Kläser, C. Schmid, C.-L. Liu.
  Action Recognition by Dense Trajectories, in: IEEE Conference on Computer Vision & Pattern Recognition, Colorado Springs, United States, June 2011, pp. 3169-3176.
  http://hal.inria.fr/inria-00583818/en
• 100C. Wojek, B. Schiele.
  A performance evaluation of single and multi-feature people detection, in: DAGM Symposium Pattern Recognition, 2008.
• 101Y. Wu.
  Mining Actionlet Ensemble for Action Recognition with Depth Cameras, in: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA, CVPR '12, IEEE Computer Society, 2012, pp. 1290–1297.
  http://dl.acm.org/citation.cfm?id=2354409.2354966
• 102L. Xia, J. Aggarwal.
  Spatio-temporal Depth Cuboid Similarity Feature for Activity Recognition Using Depth Camera, in: CVPR, 2013.
• 103J. Yang, Z. Shi, P. Vela, J. Teizer.
  Probabilistic multiple people tracking through complex situations, in: PETS workshop, 2009.
• 104L. Zhang, Y. Li, R. Nevatia.
  Global data association for multi-object tracking using network flows, in: Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, June 2008, pp. 1-8. [ DOI : 10.1109/CVPR.2008.4587584 ]
• 105Q. Zhu, S. Avidan, M. Yeh, K. Cheng.
  Fast Human Detection using a Cascade of Histograms of Oriented Gradients, in: CVPR, 2006.
• 106Y. Zhu, W. Chen, G. Guo.
  Evaluating spatiotemporal interest point features for depth-based action recognition, in: Image and Vision Computing, 2014, vol. 32, n^o 8, pp. 453 - 464. [ DOI : 10.1016/j.imavis.2014.04.005 ]
  http://www.sciencedirect.com/science/article/pii/S0262885614000651