1,721,351 research outputs found
Outdoor scene classification by a neural tree-based approach
No full textThis paper describes a Neural Tree (NT) based system for outdoor scene classification. A new NT classifier with backtracking capabilities is employed at different levels of the system architecture. First, it is used to obtain a rough interpretation of the scene by classifying each image pixel into multiple classes of static background objects, e.g. road, sky, vegetation, or into one generic class representing moving objects, e.g, vehicles, pedestrians. Then it is applied to obtain a more accurate scene interpretation by classifying all detected mobile objects into multiple classes, e.g. cars, lorries, buses, and also estimating their pose. Experiments have been performed on a large set of optical and infrared images. System performances are tested on both clean and noisy data, and comparative studies with other classifiers (i.e, a multi layer perceptron, a binary decision tree, a standard NT and a bank of neural networks) and with other scene classification methods are carried out
Object recognition and tracking for remote video surveillance
No full textIn this paper, a system for real-time object recognition and tracking for remote video surveillance is presented. In order to meet real-time requirements, a unique feature, i.e., the statistical morphological skeleton, which achieves low computational complexity, accuracy of localization, and noise robustness has been considered for both object recognition and tracking. Recognition is obtained by comparing an analytical approximation of the skeleton function extracted from the analyzed image with that obtained from model objects stored into a database. Tracking is performed by applying an extended Kalman filter to a set of observable quantities derived from the detected skeleton and other geometric characteristics of the moving object, Several experiments are shown to illustrate the validity of the proposed method and to demonstrate its usefulness in video-based applications
A real-time system for video surveillance of unattended outdoor environments
No full textThis paper describes a visual surveillance system for remote monitoring of unattended outdoor environments, The system, which works in real time, is able to detect, localize, track, and classify multiple objects moving in a surveilled area. The object classification task is based on a statistical morphological operator, the statistical pecstrum (called specstrum), which is invariant to translations, rotations, and scale variations, and it is robust to noise. Classification is performed by matching the specstrum extracted from each detected object with the specstra extracted from multiple views of different real object models contained in a large database. Outdoor images are used to test the system in real functioning conditions. Performances about good classification percentage, false and missed alarms, viewpoint invariance, noise robustness, and processing time are evaluated
Invariant feature extraction and neural trees for range surface classification
No full textIn this paper, a neural tree-based approach for classifying range images Into a set of nonoverlapping regions is presented. An Innovative procedure is applied to extract invariant surface features from each pixel of the range image. These features are 1) robust to noise, and 2) invariant to scale, shift, rotations, curvature variations, and direction of the normal. Then, a generalized neural tree is used to classify each image point as belonging to one of the six surface models of differential geometry, i.e., peak, ridge, valley, saddle, pit, and flat Comparisons with other methods and experiments on both synthetic and real three-dimensional range images have been proposed
A real-time Hough-based method for segment detection in complex multisensor images
No full textThis paper presents a real-time Hough-based algorithm for straight line segment extraction in complex multisensor images, which aims to avoid loss of spatial information as well as to eliminate spurious peaks and reduce discretization errors. A parameter space representation able to take into account spatial information during the voting phase is proposed. This representation allows the detection phase to be performed by focusing the algorithm on particular locations of the parameter space. The search space is consequently reduced, and a deeper decision strategy can be adopted, which takes into account the local distribution of segments along both a line and different lines, for comparable directions and positions. Experimental results on a large set of complex multisensor images (e.g. underwater images, low-light outdoor images, SAR images, etc.) are presented. The main advantages of the proposed method over both feature and image-space methods are evaluated in terms of computational efficiency, detection accuracy and noise robustness. (C) 2000 Academic Press
A line segment based approach for 3d motion estimation and tracking of multiple objects
No full textA line segment based approach for 3D motion estimation and tracking of multiple objects from a monocular image sequence is presented. Objects are described by means of 3D line segments, and their presence in the scene is associated with the detection of 2D line segments on the image plane. A change detection algorithm is applied to detect moving objects on the image plane and a Hough-based algorithm is used to individuate 2D line segments. 3D parameters of each line segment are estimated, at each time instant, by means of an extended Kalman filter (EKF), whose observations are the displacements of 2D line segment endpoints on the image plane. Results on both synthetic and real scenes are presented
Visual inspection of sea bottom structures by an autonomous underwater vehicle
No full textThis paper describes a vision-based system for inspections of underwater structures, e.g., pipelines, cables, etc., by an autonomous underwater vehicle (AUV). Usually, underwater inspections are performed by remote operated vehicles (ROVs) driven by human operators placed in a support vessel. However, this task is often challenging, especially in conditions of poor visibility or in presence of strong currents. The system proposed allows the AUV to accomplish the task in autonomy. Moreover, the use of a three-dimensional (3-D) model of the environment and of an extended Kalman filter (EKF) allows the guidance and the control of the vehicle in real time. Experiments done on real underwater images have demonstrated the validity of the proposed method and its efficiency in the case of critical and complex situations
Object detection and tracking in time-varying and badly illuminated outdoor environments
No full textA real-time method for object detection and tracking in outdoor environments where illumination can be very low and not constant is presented. A hierarchical (two levels) change detection method is employed to detect moving objects in the scene. At the first level, a focus-of-attention stage is applied to individuate image areas containing moving objects; at the second level, each selected image area is inspected at higher accuracy to improve the detection probability and to obtain an accurate binary reconstruction of the object shape. A background updating procedure is used to adapt the background image to the changes in the scene. Then, an extended Kalman filter is applied to track multiple objects entering the scene. Results are reported on real scenarios in the presence of shadows, occlusions, light reflections, and bad environmental conditions. (C) 1998 Society of Photo-Optical instrumentation Engineers
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