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Author: Pratim Ghosh
Publisher:
ISBN: 9781267020000
Category :
Languages : en
Pages : 138
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Book Description
Tracking objects in image sequences is a fundamental problem in computer vision. Robust tracking is critical in many vision applications, including surveillance and security systems, medical image analysis, and entertainment industry. However, the tracking problem is extremely challenging due to the high degree of uncertainty associated with the observed data. In recent years, considerable research effort has been devoted to developing solutions in controlled experimental settings.
Author: Pratim Ghosh
Publisher:
ISBN: 9781267020000
Category :
Languages : en
Pages : 138
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Book Description
Tracking objects in image sequences is a fundamental problem in computer vision. Robust tracking is critical in many vision applications, including surveillance and security systems, medical image analysis, and entertainment industry. However, the tracking problem is extremely challenging due to the high degree of uncertainty associated with the observed data. In recent years, considerable research effort has been devoted to developing solutions in controlled experimental settings.
Author: Vinay Sharma
Publisher:
ISBN:
Category : Computer vision
Languages : en
Pages : 207
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Book Description
Abstract: This thesis addresses the fundamental tasks of detecting objects in images, recovering their location, and determining their silhouette shape. We focus on object detection techniques that 1) enable simultaneous recovery of object location and object shape, 2) require minimal manual supervision during training, and 3) are capable of consistent performance under varying imaging conditions found in real-world scenarios. The work described here results in the development of a unified method for simultaneously acquiring both the location and the silhouette shape of specific object categories in outdoor scenes. The proposed algorithm integrates top-down and bottom-up processing, and combines cues from these processes in a balanced manner. The framework provides the capability to incorporate both appearance and motion information, making use of low-level contour-based features, mid-level perceptual cues, and higher-level statistical analysis. A novel Markov random field formulation is presented that effectively integrate the various cues from the top-down and bottom-up processes. The algorithm attempts to leverage the natural structure of the world, thereby requiring minimal user supervision during training. Extensive experimental evaluation shows that the approach is applicable to different object categories, and is robust to challenging conditions such as large occlusions and drastic changes in viewpoint. For static camera scenarios, we present a contour-based background-subtraction technique. Utilizing both intensity and gradient information, the algorithm constructs a fuzzy representation of foreground boundaries called a Contour Saliency Map. Combined with a low-level data-driven approach for contour completion and closure, the approach is able to accurately recover object shape. We also present object detection and segmentation approaches that combine information from visible and thermal imagery. For object detection, we present a contour-based fusion algorithm for background-subtraction. We also introduce a feature-selection approach for object segmentation from multiple imaging modalities. Starting from an incomplete segmentation from one sensor, the approach automatically extracts relevant information from other sensors to generate a complete segmentation of the object. The algorithm utilizes criteria based on Mutual Information for defining feature relevance, and does not rely on a training phase.
Author: Murtaza Safri
Publisher:
ISBN:
Category :
Languages : en
Pages : 87
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Book Description
Object tracking is an important problem with wide ranging applications. The purpose is to detect object contours and track their motion in a video. Issues of concern are to be able to map objects correctly between two frames, and to be able to track through occlusion. This thesis discusses a novel framework for the purpose of object tracking which is inspired from image registration and segmentation models.
Author: Michael Frederick Boccabella
Publisher:
ISBN:
Category : Computer vision
Languages : en
Pages : 154
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Book Description
Author: Baum, Marcus
Publisher: KIT Scientific Publishing
ISBN: 3731500787
Category : Computers
Languages : en
Pages : 190
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Book Description
This work is concerned with the simultaneous tracking and shape estimation of a mobile extended object based on noisy sensor measurements. Novel methods are developed for coping with the following two main challenges: i) The computational complexity due to the nonlinearity and high-dimensionality of the problem, and ii) the lack of statistical knowledge about possible measurement sources on the extended object.
Author: Nagachetan Bangalore Manjunathamurthy
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Efficient tracking of deformable objects moving with variable velocities is an important current research problem. In this thesis a robust tracking model is proposed for the automatic detection, recognition and tracking of target objects which are subject to variable orientations and velocities and are viewed under variable ambient lighting conditions. The tracking model can be applied to efficiently track fast moving vehicles and other objects in various complex scenarios. The tracking model is evaluated on both colour visible band and infra-red band video sequences acquired from the air by the Sussex police helicopter and other collaborators. The observations made validate the improved performance of the model over existing methods. The thesis is divided in three major sections. The first section details the development of an enhanced active contour for object segmentation. The second section describes an implementation of a global active contour orientation model. The third section describes the tracking model and assesses it performance on the aerial video sequences. In the first part of the thesis an enhanced active contour snake model using the difference of Gaussian (DoG) filter is reported and discussed in detail. An acquisition method based on the enhanced active contour method developed that can assist the proposed tracking system is tested. The active contour model is further enhanced by the use of a disambiguation framework designed to assist multiple object segmentation which is used to demonstrate that the enhanced active contour model can be used for robust multiple object segmentation and tracking. The active contour model developed not only facilitates the efficient update of the tracking filter but also decreases the latency involved in tracking targets in real-time. As far as computational effort is concerned, the active contour model presented improves the computational cost by 85% compared to existing active contour models. The second part of the thesis introduces the global active contour orientation (GACO) technique for statistical measurement of contoured object orientation. It is an overall object orientation measurement method which uses the proposed active contour model along with statistical measurement techniques. The use of the GACO technique, incorporating the active contour model, to measure object orientation angle is discussed in detail. A real-time door surveillance application based on the GACO technique is developed and evaluated on the i-LIDS door surveillance dataset provided by the UK Home Office. The performance results demonstrate the use of GACO to evaluate the door surveillance dataset gives a success rate of 92%. Finally, a combined approach involving the proposed active contour model and an optimal trade-off maximum average correlation height (OT-MACH) filter for tracking is presented. The implementation of methods for controlling the area of support of the OT-MACH filter is discussed in detail. The proposed active contour method as the area of support for the OT-MACH filter is shown to significantly improve the performance of the OT-MACH filter's ability to track vehicles moving within highly cluttered visible and infra-red band video sequences.
Author: Tomasz Adamek
Publisher:
ISBN:
Category :
Languages : en
Pages : 237
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Book Description
Author: Dennis Mitzel
Publisher:
ISBN: 9783844025248
Category : Automatic tracking
Languages : en
Pages : 198
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Book Description
Recent years have seen considerable progress in automotive safety and autonomous navigation applications, fueled by the remarkable advance of individual Computer Vision components, such as object detection, tracking, stereo and visual odometry. The goal in such applications is to automatically infer semantic understanding from the environment, observed from a moving vehicle equipped with a camera system. The pedestrian detection and tracking components constitute an actively researched part in scene understanding, important for safe navigation, path planning, and collision avoidance. Classical tracking-by-detection approaches require a robust object detector that needs to be executed in every frame. However, the detector is typically the most computationally expensive component, especially if more than one object class needs to be detected. A first goal of this thesis was to develop a vision system based on stereo camera input that is able to detect and track multiple pedestrians in real-time. To this end, we propose a hybrid tracking system that combines a computationally cheap low-level tracker with a more complex high-level tracker. The low-level trackers are either based on level-set segmentation or stereo range data together with a point registration algorithm and are employed in order to follow individual pedestrians over time, starting from an initial object detection. In order to cope with drift and to bridge occlusions that cannot be resolved by low-level trackers, the resulting tracklet outputs are fed to a high-level multihypothesis tracker, which performs longer-term data association. With this integration we obtain a real-time tracking framework by reducing object detector applications to fewer frames or even to few small image regions when stereo data is available. Reduction of expensive detector evaluations is especially relevant for the deployment on mobile platforms, where real-time performance is crucial and computational resources are notoriously
Author: Stasinos Konstantopoulos
Publisher: Springer Science & Business Media
ISBN: 3642128416
Category : Computers
Languages : en
Pages : 433
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Book Description
This volume constitutes the refereed proceedings of the 6th Hellenic Conference on Artificial Intelligence, SETN 2010, held in Athens, Greece, in May 2010. The 28 revised full papers and 22 revised short papers presented were carefully reviewed and selected from 83 submissions. The topics include but are not restricted to adaptive systems; AI and creativity; AI architectures; artificial life; autonomous systems; data mining and knowledge discovery; hybrid intelligent systems & methods; intelligent agents, multi-agent systems; intelligent distributed systems; intelligent information retrieval; intelligent/natural interactivity, intelligent virtual environments; knowledge representation and reasoning, logic programming; knowledge-based systems; machine learning, neural nets, genetic algorithms; natural language processing; planning and scheduling; problem solving, constraint satisfaction; robotics, machine vision, machine sensing.
Author: Leonidas Deligiannidis
Publisher: Morgan Kaufmann
ISBN: 012802092X
Category : Computers
Languages : en
Pages : 646
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Book Description
Emerging Trends in Image Processing, Computer Vision, and Pattern Recognition discusses the latest in trends in imaging science which at its core consists of three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. There is significant renewed interest in each of these three fields fueled by Big Data and Data Analytic initiatives including but not limited to; applications as diverse as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. These three core topics discussed here provide a solid introduction to image processing along with low-level processing techniques, computer vision fundamentals along with examples of applied applications and pattern recognition algorithms and methodologies that will be of value to the image processing and computer vision research communities. Drawing upon the knowledge of recognized experts with years of practical experience and discussing new and novel applications Editors’ Leonidas Deligiannidis and Hamid Arabnia cover; Many perspectives of image processing spanning from fundamental mathematical theory and sampling, to image representation and reconstruction, filtering in spatial and frequency domain, geometrical transformations, and image restoration and segmentation Key application techniques in computer vision some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication Pattern recognition algorithms including but not limited to; Supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms. How to use image processing and visualization to analyze big data. Discusses novel applications that can benefit from image processing, computer vision and pattern recognition such as computational biology, biometrics, biomedical imaging, robotics, security, and knowledge engineering. Covers key application techniques in computer vision from fundamentals to mid to high level processing some of which are camera networks and vision, image feature extraction, face and gesture recognition and biometric authentication. Presents a number of pattern recognition algorithms and methodologies including but not limited to; supervised and unsupervised classification algorithms, Ensemble learning algorithms, and parsing algorithms. Explains how to use image processing and visualization to analyze big data.
