@article {12549,
	title = {A Constrained Probabilistic Petri Net Framework for Human Activity Detection in Video},
	journal = {Multimedia, IEEE Transactions on},
	volume = {10},
	year = {2008},
	month = {2008/10//},
	pages = {982 - 996},
	abstract = {Recognition of human activities in restricted settings such as airports, parking lots and banks is of significant interest in security and automated surveillance systems. In such settings, data is usually in the form of surveillance videos with wide variation in quality and granularity. Interpretation and identification of human activities requires an activity model that a) is rich enough to handle complex multi-agent interactions, b) is robust to uncertainty in low-level processing and c) can handle ambiguities in the unfolding of activities. We present a computational framework for human activity representation based on Petri nets. We propose an extension-Probabilistic Petri Nets (PPN)-and show how this model is well suited to address each of the above requirements in a wide variety of settings. We then focus on answering two types of questions: (i) what are the minimal sub-videos in which a given activity is identified with a probability above a certain threshold and (ii) for a given video, which activity from a given set occurred with the highest probability? We provide the PPN-MPS algorithm for the first problem, as well as two different algorithms (naive PPN-MPA and PPN-MPA) to solve the second. Our experimental results on a dataset consisting of bank surveillance videos and an unconstrained TSA tarmac surveillance dataset show that our algorithms are both fast and provide high quality results.},
	keywords = {activity, dataset;automated, detection;human, interactions;security, net;human, nets;image, Petri, probabilistic, processing;multiagent, processing;video, representation;low-level, representation;video, signal, Surveillance, surveillance;, systems;constrained, systems;surveillance, tarmac, TSA, videos;Petri},
	isbn = {1520-9210},
	doi = {10.1109/TMM.2008.2001369},
	author = {Albanese, M. and Chellapa, Rama and Moscato, V. and Picariello, A. and V.S. Subrahmanian and Turaga,P. and Udrea,O.}
}
@conference {12593,
	title = {Coarse-to-Fine Event Model for Human Activities},
	booktitle = {Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on},
	volume = {1},
	year = {2007},
	month = {2007/04//},
	pages = {I-813 -I-816 - I-813 -I-816},
	abstract = {We analyze coarse-to-fine hierarchical representation of human activities in video sequences. It can be used for efficient video browsing and activity recognition. Activities are modeled using a sequence of instantaneous events. Events in activities can be represented in a coarse-to-fine hierarchy in several ways, i.e., there may not be a unique hierarchical structure. We present five criteria and quantitative measures for evaluating their effectiveness. The criteria are minimalism, stability, consistency, accessibility and applicability. It is desirable to develop activity models that rank highly on these criteria at all levels of hierarchy. In this paper, activities are represented as sequence of event probabilities computed using the hidden Markov model framework. Two aspects of hierarchies are analyzed: the effect of reduced frame rate on the accuracy of events detected at a finer scale; and the effect of reduced spatial resolution on activity recognition. Experiments using the UCF indoor human action dataset and the TSA airport tarmac surveillance dataset show encouraging results},
	keywords = {action, activities;spatial, airport, browsing;video, dataset;activity, dataset;UCF, event, framework;human, human, indoor, Markov, model, model;event, models;image, probabilities;hidden, processing;, recognition;coarse-to-fine, reduction;video, representation;image, resolution, resolution;image, sequences;hidden, sequences;stability;video, signal, Surveillance, tarmac, TSA},
	doi = {10.1109/ICASSP.2007.366032},
	author = {Cuntoor, N.P. and Chellapa, Rama}
}
