We present a novel representation and method for detecting and explaining anomalous activities in a video stream. Drawing from natural language processing, we introduce a representation of activities as bags of event n-grams, where we analyze the global structural information of activities using their local event statistics. We demonstrate how maximal cliques in an undirected edge-weighted graph of activities, can be used in an unsupervised manner, to discover regular sub-classes of an activity class. Based on these discovered sub-classes, we formulate a definition of anomalous activities and present a way to detect them. Finally, we characterize each discovered sub-class in terms of its "most representative member" and present an information-theoretic method to explain the detected anomalies in a human-interpretable form.

A gait-recognition technique that recovers static body and stride parameters of subjects as they walk is presented. This approach is an example of an activity-specific biometric: a method of extracting identifying properties of an individual or of an individual's behavior that is applicable only when a person is performing that specific action. To evaluate our parameters, we derive an expected confusion metric (related to mutual information), as opposed to reporting a percent correct with a limited database. This metric predicts how well a given feature vector will filter identity in a large population. We test the utility of a variety of body and stride parameters recovered in different viewing conditions on a database consisting of 15 to 20 subjects walking at both an angled and frontal-parallel view with respect to the camera, both indoors and out. We also analyze motion-capture data of the subjects to discover whether confusion in the parameters is inherently a physical or a visual measurement error property.

A method for temporally integrating appearance-based body-part labelling is presented. We begin by modifying the silhouette labelling method of Ghost (Haritaoglu, Harwood, and Davis. 1998); that system first determines which posture best describes the person currently and then uses posture-specific heuristics to generate labels for head, hands, and feet. Our approach is to assign a posture probability and then estimate body part locations for all possible postures. Next we temporally integrate these estimates by finding a best path through the posture-time lattice. A density-sampling propagation approach is used that allows us to model the multiple hypotheses resulting from consideration of different postures. We show quantitative and qualitative results where the temporal integration solution improves the instantaneous estimates. This method can be applied to any system that inherently has multiple methods of asserting instantaneous properties but from which a temporally coherent interpretation is desired.