@conference {12484,
	title = {Group motion segmentation using a Spatio-Temporal Driving Force Model},
	booktitle = {Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on},
	year = {2010},
	month = {2010/06//},
	pages = {2038 - 2045},
	abstract = {We consider the {\textquoteleft}group motion segmentation{\textquoteright} problem and provide a solution for it. The group motion segmentation problem aims at analyzing motion trajectories of multiple objects in video and finding among them the ones involved in a {\textquoteleft}group motion pattern{\textquoteright}. This problem is motivated by and serves as the basis for the {\textquoteleft}multi-object activity recognition{\textquoteright} problem, which is currently an active research topic in event analysis and activity recognition. Specifically, we learn a Spatio-Temporal Driving Force Model to characterize a group motion pattern and design an approach for segmenting the group motion. We illustrate the approach using videos of American football plays, where we identify the offensive players, who follow an offensive motion pattern, from motions of all players in the field. Experiments using GaTech Football Play Dataset validate the effectiveness of the segmentation algorithm.},
	keywords = {activity, American, analysis;image, dataset;group, driving, football, force, model;image, MOTION, pattern;group, play, plays;GaTech, problem;spatiotemporal, recognition, recognition;image, segmentation;, segmentation;motion, trajectories;multiobject},
	doi = {10.1109/CVPR.2010.5539880},
	author = {Ruonan Li and Chellapa, Rama}
}
@conference {12473,
	title = {Recognizing offensive strategies from football videos},
	booktitle = {Image Processing (ICIP), 2010 17th IEEE International Conference on},
	year = {2010},
	month = {2010/09//},
	pages = {4585 - 4588},
	abstract = {We address the problem of recognizing offensive play strategies from American football play videos. Specifically, we propose a probabilistic model which describes the generative process of an observed football play and takes into account practical issues in real football videos, such as difficulty in identifying offensive players, view changes, and tracking errors. In particular, we exploit the geometric properties of nonlinear spaces of involved variables and design statistical models on these manifolds. Then recognition is performed via {\textquoteright}analysis-by-synthesis{\textquoteright} technique. Experiments on a newly established dataset of American football videos demonstrate the effectiveness of the approach.},
	keywords = {American, analysis;video, changes;image, errors;variables, football, identification;tracking, models;geometric, models;view, play, players, processing;, properties;nonlinear, recognition;offensive, recognition;statistical, signal, spaces;offensive, statistical, strategies, technique;design, videos;analysis-by-synthesis},
	doi = {10.1109/ICIP.2010.5652192},
	author = {Ruonan Li and Chellapa, Rama}
}
@conference {12519,
	title = {Learning multi-modal densities on Discriminative Temporal Interaction Manifold for group activity recognition},
	booktitle = {Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on},
	year = {2009},
	month = {2009/06//},
	pages = {2450 - 2457},
	abstract = {While video-based activity analysis and recognition has received much attention, existing body of work mostly deals with single object/person case. Coordinated multi-object activities, or group activities, present in a variety of applications such as surveillance, sports, and biological monitoring records, etc., are the main focus of this paper. Unlike earlier attempts which model the complex spatial temporal constraints among multiple objects with a parametric Bayesian network, we propose a Discriminative Temporal Interaction Manifold (DTIM) framework as a data-driven strategy to characterize the group motion pattern without employing specific domain knowledge. In particular, we establish probability densities on the DTIM, whose element, the discriminative temporal interaction matrix, compactly describes the coordination and interaction among multiple objects in a group activity. For each class of group activity we learn a multi-modal density function on the DTIM. A Maximum a Posteriori (MAP) classifier on the manifold is then designed for recognizing new activities. Experiments on football play recognition demonstrate the effectiveness of the approach.},
	keywords = {a, activities;parametric, activity, analysis;belief, analysis;pattern, Bayesian, classification;image, classifier;multimodal, complex, constraints;data-driven, density, equipment;video, function;multiobject, interaction, manifold;discriminative, matrix;football, MOTION, network;video-based, networks;image, play, posteriori, processing;, recognition, recognition;group, recognition;maximum, signal, spatial, strategy;discriminative, temporal},
	doi = {10.1109/CVPR.2009.5206676},
	author = {Ruonan Li and Chellapa, Rama and Zhou,S. K}
}
@conference {12521,
	title = {Recognizing coordinated multi-object activities using a dynamic event ensemble model},
	booktitle = {Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on},
	year = {2009},
	month = {2009/04//},
	pages = {3541 - 3544},
	abstract = {While video-based activity analysis and recognition has received broad attention, existing body of work mostly deals with single object/person case. Modeling involving multiple objects and recognition of coordinated group activities, present in a variety of applications such as surveillance, sports, biological records, and so on, is the main focus of this paper. Unlike earlier attempts which model the complex spatial temporal constraints among different activities of multiple objects with a parametric Bayesian network, we propose a dynamic dasiaevent ensemblepsila framework as a data-driven strategy to characterize the group motion pattern without employing any specific domain knowledge. In particular, we exploit the Riemannian geometric property of the set of ensemble description functions and develop a compact representation for group activities on the ensemble manifold. An appropriate classifier on the manifold is then designed for recognizing new activities. Experiments on football play recognition demonstrate the effectiveness of the framework.},
	keywords = {activities, activity, analysis;Bayes, Bayesian, classification;video, description, ensemble, event, framework;dynamic, functions;ensemble, geometric, manifold;football, methods;geometry;image, model;ensemble, multiobject, network;video-based, play, processing;, property;classifier;coordinated, recognition;data-driven, recognition;parametric, Riemannian, signal, strategy;dynamic},
	doi = {10.1109/ICASSP.2009.4960390},
	author = {Ruonan Li and Chellapa, Rama}
}
