Trajectory Series Analysis based Event Rule Induction for Visual Surveillance

In this paper, a generic rule induction framework based on trajectory series analysis is proposed to learn the event rules. First the trajectories acquired by a tracking system are mapped into a set of primitive events that represent some basic motion patterns of moving object. Then a min-imum description length (MDL) principle based grammar induction algorithm is adopted to infer the meaningful rules from the primitive event series. Compared with previous grammar rule based work on event recognition where the rules are all defined manually, our work aims to learn the event rules automatically. Experiments in a traffic cross-road have demonstrated the effectiveness of our methods. Shown in the experimental results, most of the grammar rules obtained by our algorithm are consistent with the ac-tual traffic events in the crossroad. Furthermore the traffic lights rule in the crossroad can also be leaned correctly with the help of eliminating the irrelevant trajectories. 1

A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects

Tracking humans that are interacting with the other subjects or environment remains unsolved in visual tracking, because the visibility of the human of interests in videos is unknown and might vary over time. In particular, it is still difficult for state-of-the-art human trackers to recover complete human trajectories in crowded scenes with frequent human interactions. In this work, we consider the visibility status of a subject as a fluent variable, whose change is mostly attributed to the subject's interaction with the surrounding, e.g., crossing behind another object, entering a building, or getting into a vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the causal-effect relations between an object's visibility fluent and its activities, and develop a probabilistic graph model to jointly reason the visibility fluent change (e.g., from visible to invisible) and track humans in videos. We formulate this joint task as an iterative search of a feasible causal graph structure that enables fast search algorithm, e.g., dynamic programming method. We apply the proposed method on challenging video sequences to evaluate its capabilities of estimating visibility fluent changes of subjects and tracking subjects of interests over time. Results with comparisons demonstrate that our method outperforms the alternative trackers and can recover complete trajectories of humans in complicated scenarios with frequent human interactions.Comment: accepted by CVPR 201

Anomaly Detection, Rule Adaptation and Rule Induction Methodologies in the Context of Automated Sports Video Annotation.

Automated video annotation is a topic of considerable interest in computer vision due to its applications in video search, object based video encoding and enhanced broadcast content. The domain of sport broadcasting is, in particular, the subject of current research attention due to its fixed, rule governed, content. This research work aims to develop, analyze and demonstrate novel methodologies that can be useful in the context of adaptive and automated video annotation systems. In this thesis, we present methodologies for addressing the problems of anomaly detection, rule adaptation and rule induction for court based sports such as tennis and badminton. We first introduce an HMM induction strategy for a court-model based method that uses the court structure in the form of a lattice for two related modalities of singles and doubles tennis to tackle the problems of anomaly detection and rectification. We also introduce another anomaly detection methodology that is based on the disparity between the low-level vision based classifiers and the high-level contextual classifier. Another approach to address the problem of rule adaptation is also proposed that employs Convex hulling of the anomalous states. We also investigate a number of novel hierarchical HMM generating methods for stochastic induction of game rules. These methodologies include, Cartesian product Label-based Hierarchical Bottom-up Clustering (CLHBC) that employs prior information within the label structures. A new constrained variant of the classical Chinese Restaurant Process (CRP) is also introduced that is relevant to sports games. We also propose two hybrid methodologies in this context and a comparative analysis is made against the flat Markov model. We also show that these methods are also generalizable to other rule based environments

A novel Markov logic rule induction strategy for characterizing sports video footage

The grounding of high-level semantic concepts is a key requirement of video annotation systems. Rule induction can thus constitute an invaluable intermediate step in characterizing protocol-governed domains, such as broadcast sports footage. We here set out a novel “clause grammar template” approach to the problem of rule-induction in video footage of court games that employs a second-order meta grammar for Markov Logic Network construction. The aim is to build an adaptive system for sports video annotation capable, in principle, both of learning ab initio and also adaptively transferring learning between distinct rule domains. The method is tested with respect to both a simulated game predicate generator and also real data derived from tennis footage via computer-vision based approaches including HOG3D based player-action classification, Hough-transform based court detection, and graph-theoretic ball-tracking. Experiments demonstrate that the method exhibits both error resilience and learning transfer in the court domain context. Moreover the clause template approach naturally generalizes to any suitably-constrained, protocol-governed video domain characterized by feature noise or detector error

Trajectory Analysis and Semantic Region Modeling Using A Nonparametric Bayesian Model

We propose a novel nonparametric Bayesian model, Dual Hierarchical Dirichlet Processes (Dual-HDP), for trajectory analysis and semantic region modeling in surveillance settings, in an unsupervised way. In our approach, trajectories are treated as documents and observations of an object on a trajectory are treated as words in a document. Trajectories are clustered into different activities. Abnormal trajectories are detected as samples with low likelihoods. The semantic regions, which are intersections of paths commonly taken by objects, related to activities in the scene are also modeled. Dual-HDP advances the existing Hierarchical Dirichlet Processes (HDP) language model. HDP only clusters co-occurring words from documents into topics and automatically decides the number of topics. Dual-HDP co-clusters both words and documents. It learns both the numbers of word topics and document clusters from data. Under our problem settings, HDP only clusters observations of objects, while Dual-HDP clusters both observations and trajectories. Experiments are evaluated on two data sets, radar tracks collected from a maritime port and visual tracks collected from a parking lot