Visual surveillance by dynamic visual attention method

This paper describes a method for visual surveillance based on biologically motivated dynamic visual attention in video image sequences. Our system is based on the extraction and integration of local (pixels and spots) as well as global (objects) features. Our approach defines a method for the generation of an active attention focus on a dynamic scene for surveillance purposes. The system segments in accordance with a set of predefined features, including gray level, motion and shape features, giving raise to two classes of objects: vehicle and pedestrian. The solution proposed to the selective visual attention problem consists of decomposing the input images of an indefinite sequence of images into its moving objects, defining which of these elements are of the user's interest at a given moment, and keeping attention on those elements through time. Features extraction and integration are solved by incorporating mechanisms of charge and discharge-based on the permanency effect-, as well as mechanisms of lateral interaction. All these mechanisms have proved to be good enough to segment the scene into moving objects and background.

[1]  Dietmar Heinke,et al.  Modelling visual search experiments: the selective attention for identification model (SAIM) , 2002, Neurocomputing.

[2]  Stuart J. Russell,et al.  Object Identification: A Bayesian Analysis with Application to Traffic Surveillance , 1998, Artif. Intell..

[3]  Trevor Darrell,et al.  Robust, real-time people tracking in open environments using integrated stereo, color, and face detection , 1998, Proceedings 1998 IEEE Workshop on Visual Surveillance.

[4]  Hans-Hellmut Nagel,et al.  Incremental recognition of traffic situations from video image sequences , 2000, Image Vis. Comput..

[5]  Frederic Fol Leymarie,et al.  Tracking Deformable Objects in the Plane Using an Active Contour Model , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Antonio Fernández-Caballero,et al.  Lateral interaction in accumulative computation: a model for motion detection , 2003, Neurocomputing.

[7]  Stephen J. Maybank,et al.  Visual Surveillance for Moving Vehicles , 1998, International Journal of Computer Vision.

[8]  Alberto Machì,et al.  Automatic Visual Control of a Pedestrian Traffic Light , 1996, MVA.

[9]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.

[10]  Kurt Graf,et al.  The Prevention of Vandalism in Metro Stations , 1999 .

[11]  Michael C. Mozer,et al.  Perception of multiple objects - a connectionist approach , 1991, Neural network modeling and connectionism.

[12]  Shaun P. Vecera,et al.  Toward a Biased Competition Account of Object-Based Segregation and Attention , 2000 .

[13]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[14]  Antonio Fernández-Caballero,et al.  On motion detection through a multi-layer neural network architecture , 2003, Neural Networks.

[15]  Shmuel Peleg,et al.  A Three-Frame Algorithm for Estimating Two-Component Image Motion , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[17]  Jean-Marc Blosseville,et al.  Image Processing for Traffic Management , 1999 .

[18]  Antonio Fernández-Caballero,et al.  Spatio-temporal shape building from image sequences using lateral interaction in accumulative computation , 2003, Pattern Recognit..

[19]  Keith D. Baker,et al.  Automatic Visual Surveillance of Vehicles and People , 1999 .

[20]  M. Posner,et al.  Images of mind , 1994 .

[21]  Erik L. Dagless,et al.  A Parallel Processing Model for Real-Time Computer Vision-Aided Road Traffic Monitoring , 1992, Parallel Process. Lett..

[22]  Ph Briquet.  Video processing applied to road and urban traffic monitoring , 1992 .

[23]  Antonio Fernández-Caballero,et al.  Neurally Inspired Mechanisms for the Dynamic Visual Attention Map Generation Task , 2003, IWANN.

[24]  Carlo S. Regazzoni,et al.  Advanced Video-Based Surveillance Systems , 1998 .

[25]  Filiberto Pla,et al.  Motion-based segmentation and region tracking in image sequences , 2001, Pattern Recognit..

[26]  Hilary Buxton,et al.  Analogical Representation of Spatial Events for Understanding Traffic Behaviour , 1992, ECAI.

[27]  R. M. Inigo,et al.  Application of machine vision to traffic monitoring and control , 1989 .

[28]  K. W. Dickinson,et al.  Road traffic monitoring using the TRIP II system , 1989 .

[29]  Valerio Recagno,et al.  Security in Ports: the User Requirements for Surveillance System , 1999 .

[30]  Senén Barro,et al.  Local Accumulation of Persistent Activity at Synaptic Level: Application to Motion Analysis , 1995, IWANN.

[31]  Mahmood Fathy,et al.  Real-time image processing approach to measure traffic queue parameters , 1995 .

[32]  Antonio Fernández-Caballero,et al.  Length-speed ratio (LSR) as a characteristic for moving elements real-time classification , 2003, Real Time Imaging.

[33]  A. Anzalone,et al.  Video-based Management of Traffic Light at Pedestrian Road Crossing , 1999 .

[34]  Antonio Fernández-Caballero,et al.  Segmentation from motion of non-rigid objects by neuronal lateral interaction , 2001, Pattern Recognit. Lett..

[35]  Francisco Sandoval,et al.  From Natural to Artificial Neural Computation , 1995 .

[36]  Osama Masoud,et al.  Detection and classification of vehicles , 2002, IEEE Trans. Intell. Transp. Syst..

[37]  David C. Hogg,et al.  An efficient method for contour tracking using active shape models , 1994, Proceedings of 1994 IEEE Workshop on Motion of Non-rigid and Articulated Objects.

[38]  M. Sakauchi,et al.  Measurement of traffic flow using real time processing of moving pictures , 1982, 32nd IEEE Vehicular Technology Conference.

[39]  G. Backer,et al.  Two selection stages provide efficient object-based attentional control for dynamic vision , 2003 .

[40]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[41]  Marcello Pellegrini,et al.  Highway traffic monitoring , 1999 .

[42]  José Mira Mira,et al.  Permanence Memory: A System for Real Time Motion Analysis in Image Sequences , 1992, MVA.

[43]  Jordan Grafman,et al.  Handbook of Neuropsychology , 1991 .