Knowledge modelling for the motion detection task

In this article knowledge modelling at the knowledge level for the task of moving objects detection in image sequences is introduced. Three items have been the focus of the approach: (1) the convenience of knowledge modelling of tasks and methods in terms of a library of reusable components and in advance to the phase of operationalization of the primitive inferences; (2) the potential utility of looking for inspiration in biology; (3) the convenience of using these biologically inspired problem-solving methods (PSMs) to solve motion detection tasks. After studying a summary of the methods used to solve the motion detection task, the moving targets in indefinite sequences of images detection task is approached by means of the algorithmic lateral inhibition (ALI) PSM. The task is decomposed in four subtasks: (a) thresholded segmentation; (b) motion detection; (c) silhouettes parts obtaining; and (d) moving objects silhouettes fusion. For each one of these subtasks, first, the inferential scheme is obtained and then each one of the inferences is operationalized. Finally, some experimental results are presented along with comments on the potential value of our approach

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

Lateral interaction in accumulative computation

To be able to understand the motion of non-rigid objects, techniques in image processing and computer vision are essential for motion analysis. Lateral interaction in accumulative computation for extracting non-rigid blobs and shapes from an image sequence has recently been presented, as well as its application to segmentation from motion. In this paper we show an architecture consisting of five layers based on spatial and temporal coherence in visual motion analysis with application to visual surveillance. The LIAC method used in general task ?spatio-temporal coherent shape building? consists in (a) spatial coherence for brightness-based image segmentation, (b) temporal coherence for motion-based pixel charge computation, (c) spatial coherence for charge-based pixel charge computation, (d) spatial coherence for charge-based blob fusion, and, (e) spatial coherence for charge-based shape fusion. In our case, temporal coherence (in accumulative computation) is understood as a measure of frame to frame motion persistency on a pixel, whilst spatial coherence (in lateral interaction) is a measure of pixel to neighbouring pixels accumulative charge comparison

Computational agents to model knowledge - theory, and practice in visual surveillance.

In this work the concept of computational agent is located within the methodological framework of levels and domains of description of a calculus in the context of different usual paradigms in Artificial Intelligence (symbolic, situated, connectionist, and hybrid). Emphasis in the computable aspects of agent theory is put, leaving open the possibility to the incorporation of other aspects that are still pure cognitive nomenclature without any computational counterpart of equivalent semantic richness. These ideas are currently being implemented on semi-automatic video-surveillance

Dynamic visual attention model in image sequences

A new computational architecture of dynamic visual attention is introduced in this paper. Our approach defines a model for the generation of an active attention focus on a dynamic scene captured from a still or moving camera. The aim is to obtain the objects that keep the observer?s attention in accordance with a set of predefined features, including color, motion and shape. The solution proposed to the selective visual attention problem consists in decomposing the input images of an indefinite sequence of images into its moving objects, by defining which of these elements are of the user?s interest, and by keeping attention on those elements through time. Thus, the three tasks involved in the attention model are introduced. The Feature-Extraction task obtains those features (color, motion and shape features) necessary to perform object segmentation. The Attention-Capture task applies the criteria established by the user (values provided through parameters) to the extracted features and obtains the different parts of the objects of potential interest. Lastly, the Attention-Reinforcement task maintains attention on certain elements (or objects) of the image sequence that are of real interest

Motion features to enhance scene segmentation in active visual attention

A new computational model for active visual attention is introduced in this paper. The method extracts motion and shape features from video image sequences, and integrates these features to segment the input scene. The aim of this paper is to highlight the importance of the motion features present in our algorithms in the task of refining and/or enhancing scene segmentation in the method proposed. The estimation of these motion parameters is performed at each pixel of the input image by means of the accumulative computation method, using the so-called permanency memories. The paper shows some examples of how to use the ?motion presence?, ?module of the velocity? and ?angle of the velocity? motion features, all obtained from accumulative computation method, to adjust different scene segmentation outputs in this dynamic visual attention method

Motion-based stereovision model with potential utility in robot navigation.

Autonomous robot guidance in dynamic environments requires, on the one hand, the study of relative motion of the objects of the environment with respect to the robot, and on the other hand, the analysis of the depth towards those objects. In this paper, a stereo vision method, which combines both topics with potential utility in robot navigation, is proposed. The goal of the stereo vision model is to calculate depth of surrounding objects by measuring the disparity between the two-dimensional imaged positions of the object points in a stereo pair of images. The simulated robot guidance algorithm proposed starts from the motion analysis that occurs in the scene and then establishes correspondences and analyzes the depth of the objects. Once these steps have been performed, the next step is to induce the robot to take the direction where objects are more distant in order to avoid obstacles

Modelling the stereovision-correspondence-analysis task by lateral inhibition in accumulative computation problem-solving method.

Recently, the Algorithmic Lateral Inhibition (ALI) method and the Accumulative Computation (AC) method have proven to be efficient in modelling at the knowledge level for general-motion-detection tasks in video sequences. More precisely, the task of persistent motion detection has been widely expressed by means of the AC method, whereas the ALI method has been used with the objective of moving objects detection, labelling and further tracking. This paper exploits the current knowledge of our research team on the mentioned problem-solving methods to model the Stereovision-Correspondence-Analysis (SCA) task. For this purpose, ALI and AC methods are combined into the Lateral Inhibition in Accumulative Computation (LIAC) method. The four basic subtasks, namely ?LIAC 2D Charge-Memory Calculation?, ?LIAC 2D Charge-Disparity Analysis? and ?LIAC 3D Charge-Memory Calculation? in our proposal of SCA are described in detail by inferential CommonKADS schemes. It is shown that the LIAC method may perfectly be used to solve a complex task based on motion information inherent to binocular video sequences

Permanency memories in scene depth analysis

There are several strategies of how to retrieve depth information from a sequence of images, like depth from motion, depth from shading and depth from stereopsis. In this paper, we introduce a new method to retrieve depth based on motion and stereopsis. A motion detection representation helps establishing further correspondences between different motion information. This representation bases in the permanency memories mechanism, where jumps of pixels between grey level bands are computed in a matrix of charge accumulators. For each frame of a video stereovision sequence, the method fixes the right permanency stereo memory, and displaces the left permanency stereo memory by pixel on the epipolar restriction basis over the right one, in order to analyze the disparities of the motion trails calculated. By means of this functionality, for all possible displacements of one permanency memory over the other, the correspondences between motion trails are checked, and the disparities are assigned, providing a way to analyze the depths of the objects present in the scene