Activity analysis; social behavior; interactions; video; cell-phone; social media; language processing; data mining; topic models; sequence models; context modeling; large-scale sensing
Farrahi Katayoun, Gatica-Perez Daniel (2014), A probabilistic approach to mining mobile phone data sequences, in Personal and Ubiquitous Computing
, 18(1), 223-238.
Malmi Eric, Do Trinh Minh Tri, Gatica-Perez Daniel (2013), From Foursquare to My Square: Learning Check-in Behavior from Multiple Sources, in ICWSM
Santani Darshan, Gatica-Perez Daniel (2013), Revisiting the generality of the rank-based human mobility model, in Ubicomp (Adjunct Publication)
Santani Darshan, Gatica-Perez Daniel (2013), Speaking swiss: languages and venues in foursquare, in ACM Multimedia
Jagan Varadarajan, Rémi Emonet, Jean-Marc Odobez, Farrahi K., Pentland A. (2012), Bridging the past, present and future: Modeling scene activities from event relationships and global rules, in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2012
Malmi E., Do T., Gatica-Perez D. (2012), Checking In or Checked In: Comparing Large-Scale Manual and Automatic Location Disclosure Patterns, in Proc. Int. Conf. on Mobile and Ubiquitous Multimedia (MUM), Ulm, Oral Presentation
Farrahi K., Gatica-Perez D. (2012), Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model, in Proc. IEEE Int. Symp. on Wearable Computers (ISWC)
Negoescu R., Gatica-Perez D., Odobez J-M (2012), Flickr groups: Multimedia communities for multimedia analysis, Bentham Science Publishers, Unknown, 234-250.
Farrahi K., Emonet R., Odobez J.-M. (2011), A Probabilistic Approach to Socio-Geographic Reality Mining
, PhD Thesis, EPFL, Lausanne.
Farrahi K., Gatica-Perez D., Gatica-Perez D. (2011), Discovering Routines from Large-Scale Human Locations using Probabilistic Topic Models, in ACM Transactions on Intelligent Systems and Technology, Special Issue on Activity Recognition
, 2(1), 137-162.
Emonet R, Varadarajan J, Odobez J-M (2011), Extracting and locating temporal motifs in video scenes using a hierarchical non parametric Bayesian model, in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011
, Providence, Rhode IslandProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Colorado Springs.
Negoescu R. (2011), Modeling and Understanding Communities in Online Social Media using Probabilistic Methods
, PhD Thesis, EPFL, Lausanne.
Emonet R, Varadarajan J (2011), Multi-camera open space human activity discovery for anomaly detection, in 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2011
, Klagenfurth, Austria2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2011, Klagenfuhrt, Austria.
Madan A. (2011), Pervasive Sensing to Model Political Opinions in Face-to-Face Networks, in Proc. Int. Conf. on Pervasive Computing (Pervasive)
, San FransiscoInt. Conf. on Pervasive Computing (Pervasive), San-Francisco.
Madan A., Cebrian M., Moturu S., Farrahi K., Pentland A. (2011), Sensing the Health State of our Society, in IEEE Pervasive Computing
Varadarajan J., Emonet R. (2010), A Sparsity Constraint for Topic Models: Application to Temporal Activity Mining., in NIPS Workshop on Workshop on Practical Applications of Sparse Modeling: Open Issues and New Directio
, Vancouver, CanadaNIPS workshop on Practical Application of Sparse Modeling: Open Issues and New Directions, Vancouver, Canada.
Negoescu R., Loui A., Gatica-Perez D., Pentland A. (2010), Kodak Moments and Flickr Diamonds: How Users Shape Large-Scale Media, in Proc. ACM Int. Conf. on Multimedia (ACM MM),
, Florence, ItalieACM Int. Conf. on Multimedia (ACM MM), Firenze.
Farrahi Katayoun, Gatica-Perez daniel (2010), Mining Human Location-Routines Using a Multi-Level Approach to Topic Modeling, in IEEE Int. Conference on Social Computing, Symposium on Social Intelligence and Networking (SocialCom
, Proceedings of the IEEE International Conference on Social Computing, SIN Symposium, Minneapolis, USA.
Negoescu Radu-Andrei, Gatica-Perez Daniel (2010), Modeling and Understanding Flickr Communities through Topic-based Analysis, in IEEE Transactions on Multimedia
, 12(5), 399-416.
Varadarajan J., Emonet R., Odobez J.-M. (2010), Probabilistic Latent Sequential Motifs: Discovering Temporal Activity Patterns in Video Scenes, in Proc. British Machine and Vision Conference (BMVC), Sept. 2010
, AberystwythBritish Machine Vision Conference (BMVC), Aberystwyth, UK.
Jagan Varadarajan, Rémi Emonet, Jean-Marc Odobez, A sequential topic model for mining recurrent activities from long term video logs, in Int. Journal of Computer Vision (IJCV)
Understanding human behavior is one of the most intriguing and fascinating research domains, which encompasses several research fields, ranging from economics and sociology to more recently computer science. Immense progress in sensor and communication technologies has led to the development of devices and systems recording daily human activities in both real and virtual (web-based) settings. This has led to an increase of research on the design of algorithms capable of inferring meaningful behavioral patterns of human activities from the information contained in data logs or captured by sensors. Simultaneously, there are new application opportunities in many domains such as surveillance, health care monitoring, social networking, and recommendation systems.The aim of the HAI project is to investigate the above domain by performing long-term research which addresses fundamental questions and common tasks of this domain: how to design robust features for accurate activity/interaction representation? How to learn or discover activity patterns, introduce hierarchies or temporal order at different scales, and deal efficiently with large amounts of data? How to infer contextual information that affects activity patterns or their occurrence, or facilitates their interpretation. To achieve these goals, we will investigate new approaches by anchoring the design of general activity models in the context of four different and relatively recent application domains with specific scenarios, types of activities, and data modalities.HAI-1: Activity analysis from long term video recordings. The goal is to automatically discover the typical activities of moving entities (cars, people, groups), their characteristics, the relations between them within and across cameras, and detect abnormal activities. These goals will be reached by combining sequential state models at different levels with data mining tools relying on co-occurrence analysis.HAI-2: Activity analysis from mobile phone data. The aim is the design of novel heterogenous data representations and probabilistic models for the modeling of varying time duration routines for location and proximity based activity discovery, for the identification of large scale human communication patterns based on phone calls or text messaging, and for the discovery of individual's life patterns from a rich set of phone data modalities.HAI-3: Community activity analysis in social media. The goal is to investigate the structure, evolution, and practices of communities in social media with Flick as a target. In particular, using statistical models relying on textual and social metadata, the project will model the dynamical aspect of social media groups (including topic and memberships patterns), study and discover micro-activity patterns within sub-groups, and investigate the use of visual information extracted from photos and videos to refine user and group descriptions.HAI-4: Context modeling for just-in-time multimedia information retrieval. The goal is to model the context of conversational activity for users of an Automatic Content Linking Device (ACLD), which is a just-in-time retrieval system that spontaneously displays documents and multimedia fragments based on the current topic of discussion. Context modeling will help to decide whether it is useful and appropriate to interrupt users with new results, and to estimate relevance feedback from users in order to guide upcoming searches.While each of the sub-projects pursues its own goals, the grounding of the approaches on similar principled methodologies (e.g. bag-of-words and Bayesian topic models) will provide opportunities for research synergies and the strengthening of Idiap's activities on human behavior and interaction modeling.