Abstract—During the last years, the task of automatic event analysis in video sequences has gained an increasing attention among the research community. The application domains are disparate, ranging from video surveillance to automatic video annotation for sport videos or TV shots. Whatever the application field, most of the works in event analysis are based on two main approaches: the former based on explicit event recognition, focused on finding highlevel, semantic interpretations of video sequences, and the latter based on anomaly detection. This paper deals with the second approach, where the final goal is not the explicit labeling of recognized events, but the detection of anomalous events differing from typical patterns. In particular, the proposed work addresses anomaly detection by means of trajectory analysis, an approach with several application fields, most notably video surveillance and traffic monitoring. The proposed approach is based on single-class support vector machine (SVM) clustering, where the novelty detection SVM capabilities are used for the identification of anomalous trajectories. Particular attention is given to trajectory classification in absence of a priori information on the distribution of outliers. Experimental results prove the validity of the proposed approach. Index Terms—Anomaly detection, event analysis, support vector machines (SVMs), trajectory clustering.

Trajectory classification, i.e., model construction for predicting the class labels of moving objects based on their trajectories and other features, has many important, real-world applications. A number of methods have been reported in the literature, but due to using the shapes of whole trajectories for classification, they have limited classification capability when discriminative features appear at parts of trajectories or are not relevant to the shapes of trajectories. These situations are often observed in long trajectories spreading over large geographic areas. Since an essential task for effective classification is generating discriminative features, a feature generation framework TraClass for trajectory data is proposed in this paper, which generates a hierarchy of features by partitioning trajectories and exploring two types of clustering: (1) region-based and (2) trajectory-based. The former captures the higher-level region-based features without using movement patterns, whereas the latter captures the lower-level trajectory-based features using movement patterns. The proposed framework overcomes the limitations of the previous studies because trajectory partitioning makes discriminative parts of trajectories identifiable, and the two types of clustering collaborate to find features of both regions and sub-trajectories. Experimental results demonstrate that TraClass generates high-quality features and achieves high classification accuracy from real trajectory data.

Abstract—This paper presents a survey of trajectory-based activity analysis for visual surveillance. It describes techniques that use trajectory data to define a general set of activities that are applicable to a wide range of scenes and environments. Events of interest are detected by building a generic topographical scene description from underlying motion structure as observed over time. The scene topology is automatically learned and is distinguished by points of interest and motion characterized by activity paths. The methods we review are intended for real-time surveillance through definition of a diverse set of events for further analysis triggering, including virtual fencing, speed profiling, behavior classification, anomaly detection, and object interaction. Index Terms—Event detection, motion analysis, situational awareness, statistical learning. Fig. 1. Relationship between analysis levels and required knowledge: highlevel activity analysis requires large amounts of domain knowledge while lowlevel analysis assumes very little. I.

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Recently a large amount of research has been devoted to automatic activity analysis. Typically, activities have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper evaluates different similarity measures and clustering methodologies to catalog their strengths and weaknesses when utilized for the trajectory learning problem. The clustering performance is measured by evaluating the correct clustering rate on different datasets with varying characteristics. 1.

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Abstract—Society is rapidly accepting the use of video cameras in many new and varied locations, but effective methods to utilize and manage the massive resulting amounts of visual data are only slowly developing. This paper presents a framework for live video analysis in which the behaviors of surveillance subjects are described using a vocabulary learned from recurrent motion patterns, for real-time characterization and prediction of future activities, as well as the detection of abnormalities. The repetitive nature of object trajectories is utilized to automatically build activity models in a 3-stage hierarchical learning process. Interesting nodes are learned through Gaussian mixture modeling, connecting routes formed through trajectory clustering, and spatio-temporal dynamics of activities probabilistically encoded using hidden Markov models. Activity models are adapted to small temporal variations in an online fashion using maximum likelihood regression and new behaviors are discovered from a periodic re-training for long-term monitoring. Extensive evaluation on various datasets, typically missing from other work, demonstrates the efficacy and generality of the proposed framework for surveillance-based activity analysis. Index Terms—Trajectory clustering, real-time activity analysis, abnormality detection, trajectory learning, activity prediction 1

Abstract—Past research on temporal databases has primarily focused on state-based representations and on relational query language extensions for such representations. This led to many different proposals that had did not succeed in making a significant impact on SQL-compliant DBMS. More recently however, there has been significant interest and progress on event sequences, leading to vendor-proposed extensions of SQL standards for pattern queries based on Kleene-closure expressions. In this paper, we first outline these extensions and their uses in dealing with sequence of events, and then show that they can also be used effectively to express more traditional temporal queries, such as coalescing and joins, on state-based representations. Thus, we propose an approach that takes full advantage of the fact that every state-based representation also has a dual representation based on its start-event and its end-event. I.

Extraction of activity patterns on large video

The present work presents a new method for activity extraction and reporting from video based on the aggregation of fuzzy relations. Trajectory clustering is first employed mainly to discover the points of entry and exit of mobiles appearing in the scene. In a second step, proximity relations between resulting clusters of detected mobiles and contextual elements from the scene are modeled employing fuzzy relations. These can then be aggregated employing typical soft-computing algebra. A clustering algorithm based on the transitive closure calculation of the fuzzy relations allows building the structure of the scene and characterize the ongoing different activities of the scene. Discovered activity zones can be reported as activity maps with different granularities thanks to the analysis of the transitive closure matrix. Taking advantage of the soft relation properties, activity zones and related activities can be labeled in a more human-like language. We present results obtained on real videos corresponding to apron monitoring in the Toulouse airport in France. 1.