A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

A system for learning statistical motion patterns

Analysis of motion patterns is an effective approach for anomaly detection and behavior prediction. Current approaches for the analysis of motion patterns depend on known scenes, where objects move in predefined ways. It is highly desirable to automatically construct object motion patterns which reflect the knowledge of the scene. In this paper, we present a system for automatically learning motion patterns for anomaly detection and behavior prediction based on a proposed algorithm for robustly tracking multiple objects. In the tracking algorithm, foreground pixels are clustered using a fast accurate fuzzy k-means algorithm. Growing and prediction of the cluster centroids of foreground pixels ensure that each cluster centroid is associated with a moving object in the scene. In the algorithm for learning motion patterns, trajectories are clustered hierarchically using spatial and temporal information and then each motion pattern is represented with a chain of Gaussian distributions. Based on the learned statistical motion patterns, statistical methods are used to detect anomalies and predict behaviors. Our system is tested using image sequences acquired, respectively, from a crowded real traffic scene and a model traffic scene. Experimental results show the robustness of the tracking algorithm, the efficiency of the algorithm for learning motion patterns, and the encouraging performance of algorithms for anomaly detection and behavior prediction

Pathological element-based active device models and their application to symbolic analysis

This paper proposes new pathological element-based active device models which can be used in analysis tasks of linear(ized) analog circuits. Nullators and norators along with the voltage mirror-current mirror (VM-CM) pair (collectively known as pathological elements) are used to model the behavior of active devices in voltage-, current-, and mixed-mode, also considering parasitic elements. Since analog circuits are transformed to nullor-based equivalent circuits or VM-CM pairs or as a combination of both, standard nodal analysis can be used to formulate the admittance matrix. We present a formulation method in order to build the nodal admittance (NA) matrix of nullor-equivalent circuits, where the order of the matrix is given by the number of nodes minus the number of nullors. Since pathological elements are used to model the behavior of active devices, we introduce a more efficient formulation method in order to compute small-signal characteristics of pathological element-based equivalent circuits, where the order of the NA matrix is given by the number of nodes minus the number of pathological elements. Examples are discussed in order to illustrate the potential of the proposed pathological element-based active device models and the new formulation method in performing symbolic analysis of analog circuits. The improved formulation method is compared with traditional formulation methods, showing that the NA matrix is more compact and the generation of nonzero coefficients is reduced. As a consequence, the proposed formulation method is the most efficient one reported so far, since the CPU time and memory consumption is reduced when recursive determinant-expansion techniques are used to solve the NA matrix.Promep-Mexico UATLX-PTC-088Junta de Andalucía TIC-2532Ministerio de Educación y Ciencia TEC2007-67247, TEC2010-14825UC-MEXUS-CONACyT CN-09-31

Teleoperation experiments with a Utah/MIT hand and a VPL DataGlove

A teleoperation system capable of controlling a Utah/MIT Dextrous Hand using a VPL DataGlove as a master is presented. Additionally the system is capable of running the dextrous hand in robotic (autonomous) mode as new programs are developed. The software and hardware architecture used is presented and the experiments performed are described. The communication and calibration issues involved are analyzed and applications to the analysis and development of automated dextrous manipulations are investigated

A new dromaeosaurid (Dinosauria: Theropoda) from the Upper Cretaceous Wulansuhai Formation of Inner Mongolia, China

We describe a new dromaeosaurid theropod from the Upper Cretaceous Wulansuhai Formation of Bayan Mandahu, Inner Mongolia. The new taxon, Linheraptor exquisitus gen. et sp. nov., is based on an exceptionally well-preserved, nearly complete skeleton. This specimen represents the fifth dromaeosaurid taxon recovered from the Upper Cretaceous Djadokhta Formation and its laterally equivalent strata, which include the Wulansuhai Formation, and adds to the known diversity of Late Cretaceous dromaeosaurids. Linheraptor exquisitus closely resembles the recently reported Tsaagan mangas. Uniquely among dromaeosaurids, the two taxa share a large, anteriorly located maxillary fenestra and a contact between the jugal and the squamosal that excludes the postorbital from the infratemporal fenestra. These features suggest a sister-taxon relationship between L. exquisitus and T. mangas, which indicates the presence of a unique dromaeosaurid lineage in the Late Cretaceous of Asia. A number of cranial and dental features seen in L. exquisitus and T. mangas, and particularly some postcranial features of L. exquisitus, suggest that these two taxa are probably intermediate in systematic position between known basal and derived dromaeosaurids. The discovery of Linheraptor exquisitus is thus important for understanding the evolution of some salient features seen in the derived dromaeosaurids