Statistical Models of Object Interaction
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Event Detection by Eigenvector Decomposition Using Object and Frame Features
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 7 - Volume 07
On-line trajectory clustering for anomalous events detection
Pattern Recognition Letters
Video Behavior Profiling for Anomaly Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Automatic understanding of human behavior is an important and challenging objective in several surveillance applications. One of the main problems of this task consists in accurately defining models able to characterize in a discriminative but, at the same time, enough general way people actions. In this work a bio-inspired model is proposed to represent people interactions in a Bayesian framework using their patterns of movement. Couples of observed interacting trajectories are encoded into a Dynamic Bayesian Network (DBN) model where states and conditional probability densities are learned in an online manner in order to statistically describe interactions. Observed trajectories are processed by the Instantaneous Topological Map (ITM) algorithm that automatically creates a topological map used to define the states of the DBN. The transition probabilities are estimated by combining states frequency of occurrence, evaluated by a voting-based approach, and their temporal occurrence represented by Gaussian Mixture Models. The discriminative capabilities of this model to detect interactions are shown both in a simulated and in a real-world environment.