@article {12466,
	title = {PADS: A Probabilistic Activity Detection Framework for Video Data},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {32},
	year = {2010},
	month = {2010/12//},
	pages = {2246 - 2261},
	abstract = {There is now a growing need to identify various kinds of activities that occur in videos. In this paper, we first present a logical language called Probabilistic Activity Description Language (PADL) in which users can specify activities of interest. We then develop a probabilistic framework which assigns to any subvideo of a given video sequence a probability that the subvideo contains the given activity, and we finally develop two fast algorithms to detect activities within this framework. OffPad finds all minimal segments of a video that contain a given activity with a probability exceeding a given threshold. In contrast, the OnPad algorithm examines a video during playout (rather than afterwards as OffPad does) and computes the probability that a given activity is occurring (even if the activity is only partially complete). Our prototype Probabilistic Activity Detection System (PADS) implements the framework and the two algorithms, building on top of existing image processing algorithms. We have conducted detailed experiments and compared our approach to four different approaches presented in the literature. We show that-for complex activity definitions-our approach outperforms all the other approaches.},
	keywords = {Automated;Programming Languages;Video Recording;, Computer-Assisted;Models, PADL;PADS;image processing algorithms;offPad algorithm;onPad algorithm;probabilistic activity description language;probabilistic activity detection framework;video data;video sequence;image sequences;object detection;probability;video surveillance;Algorit, Statistical;Movement;Pattern Recognition},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2010.33},
	author = {Albanese, M. and Chellapa, Rama and Cuntoor, N. and Moscato, V. and Picariello, A. and V.S. Subrahmanian and Udrea,O.}
}
@article {12645,
	title = {Matching shape sequences in video with applications in human movement analysis},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {27},
	year = {2005},
	month = {2005/12//},
	pages = {1896 - 1909},
	abstract = {We present an approach for comparing two sequences of deforming shapes using both parametric models and nonparametric methods. In our approach, Kendall{\textquoteright}s definition of shape is used for feature extraction. Since the shape feature rests on a non-Euclidean manifold, we propose parametric models like the autoregressive model and autoregressive moving average model on the tangent space and demonstrate the ability of these models to capture the nature of shape deformations using experiments on gait-based human recognition. The nonparametric model is based on dynamic time-warping. We suggest a modification of the dynamic time-warping algorithm to include the nature of the non-Euclidean space in which the shape deformations take place. We also show the efficacy of this algorithm by its application to gait-based human recognition. We exploit the shape deformations of a person{\textquoteright}s silhouette as a discriminating feature and provide recognition results using the nonparametric model. Our analysis leads to some interesting observations on the role of shape and kinematics in automated gait-based person authentication.},
	keywords = {Automated;Photography;Reproducibility of Results;Sensitivity and Specificity;Subtraction Technique;Video Recording;, autoregressive moving average model;deforming shapes;dynamic time-warping;feature extraction;gait-based human recognition;human movement analysis;matching shape sequences;video;autoregressive moving average processes;feature extraction;image matching;imag, Biological;Models, Computer-Assisted;Imaging, Statistical;Movement;Pattern Recognition, Three-Dimensional;Leg;Models},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2005.246},
	author = {A,Veeraraghavan and Roy-Chowdhury, A.K. and Chellapa, Rama}
}
@article {12647,
	title = {"Shape Activity": a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection},
	journal = {Image Processing, IEEE Transactions on},
	volume = {14},
	year = {2005},
	month = {2005/10//},
	pages = {1603 - 1616},
	abstract = {The aim is to model "activity" performed by a group of moving and interacting objects (which can be people, cars, or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include co-occurrence statistics (individual and joint histograms) and dynamic Bayesian networks, neither of which is applicable when the number of interacting objects is large. We treat the objects as point objects (referred to as "landmarks") and propose to model their changing configuration as a moving and deforming "shape" (using Kendall{\textquoteright}s shape theory for discrete landmarks). A continuous-state hidden Markov model is defined for landmark shape dynamics in an activity. The configuration of landmarks at a given time forms the observation vector, and the corresponding shape and the scaled Euclidean motion parameters form the hidden-state vector. An abnormal activity is then defined as a change in the shape activity model, which could be slow or drastic and whose parameters are unknown. Results are shown on a real abnormal activity-detection problem involving multiple moving objects.},
	keywords = {abnormal activity detection;activity recognition;co-occurrence statistics;continuous-state hidden Markov model;dynamic Bayesian networks;hidden-state vector;particle filtering;scaled Euclidean motion parameter;shape deforming;belief networks;filtering the, Automated;Subtraction Technique;Video Recording;, Biological;Models, Computer-Assisted;Information Storage and Retrieval;Markov Chains;Models, Statistical;Movement;Pattern Recognition},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2005.852197},
	author = {Vaswani, N. and Roy-Chowdhury, A.K. and Chellapa, Rama}
}
