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In distracting and cluttered environments object tracking in long duration videos is quite challenging task for computer vision. The two main difficulties in real world object tracking are partial occlusion and illumination variat...
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In distracting and cluttered environments object tracking in long duration videos is quite challenging task for computer vision. The two main difficulties in real world object tracking are partial occlusion and illumination variations. Conventional methods are based on annotation of the object in the first frame, the tracker task is to estimate the target locations using same annotations in successive video frames. But these methods fail in automatic detection and tracking of object in video. This failure task can be rectified by using real time segmentation, object detection and tracking. The aim of the survey for object racking in video based on image segmentation and pattern matching is to emphasis the robustness and accuracy. We have clearly given survey on real time segmentation, feature extraction and block matching using distance measures for real time object tracking. This survey also provides an overview of the present growth of research by summarizing promising challenges for further research.
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This paper addresses the problem of matching vehicles across multiple sightings under variations in illumination and camera poses. Since multiple observations of a vehicle are separated in large temporal and/or spatial gaps, thus ...
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This paper addresses the problem of matching vehicles across multiple sightings under variations in illumination and camera poses. Since multiple observations of a vehicle are separated in large temporal and/or spatial gaps, thus prohibiting the use of standard frame-to-frame data association, we employ features extracted over a sequence during one time interval as a vehicle fingerprint that is used to compute the likelihood that two or more sequence observations are from the same or different vehicles. Furthermore, since our domain is aerial video tracking, in order to deal with poor image quality and large resolution and quality variations, our approach employs robust alignment and match measures for different stages of vehicle matching. Most notably, we employ a heterogeneous collection of features such as lines, points, and regions in an integrated matching framework. Heterogeneous features are shown to be important. Line and point features provide accurate localization and are employed for robust alignment across disparate views. The challenges of change in pose, aspect, and appearances across two disparate observations are handled by combining a novel feature-based quasi-rigid alignment with flexible matching between two or more sequences. However, since lines and points are relatively sparse, they are not adequate to delineate the object and provide a comprehensive matching set that covers the complete object. Region features provide a high degree of coverage and are employed for continuous frames to provide a delineation of the vehicle region for subsequent generation of a match measure. Our approach reliably delineates objects by representing regions as robust blob features and matching multiple regions to multiple regions using Earth Mover's Distance (EMD). Extensive experimentation under a variety of real-world scenarios and over hundreds of thousands of Confirmatory Identification (CID) trails has demonstrated about 95 percent accuracy in vehicle reacqu-isition with both visible and Infrared (IR) imaging cameras.
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The aim of this research was to design an moving object detection, localization and tracking algorithm able to detect, localize and track especially humans and vehicles. We focused on triangulation techniques to calculate the posi...
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The aim of this research was to design an moving object detection, localization and tracking algorithm able to detect, localize and track especially humans and vehicles. We focused on triangulation techniques to calculate the position of the detected objects in a stereo vision rig coordinates frame. For objects detection and tracking the novel algorithm, based on statistical image processing methods, was proposed. Verification of a proper operation of the elaborated method was made by conducting series of experiments. Our results indicate that the algorithm localizes, detects and tracks objects accurately for the most tested conditions.
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The problem of object recognition has been considered here. Color descriptions from distinct regions covering multiple segments are considered for object representation. Distinct multicolored regions are detected using edge maps a...
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The problem of object recognition has been considered here. Color descriptions from distinct regions covering multiple segments are considered for object representation. Distinct multicolored regions are detected using edge maps and clustering. Performance of the proposed methodologies has been evaluated on three data sets and the results are found to be better than existing methods when a small number of training views is considered.
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The use of the migrant principle has proved to be effective in reducing the impact of the initial populations of genetic algorithms in optimising simple linear functions. Analytical and empirical results have also suggested that t...
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The use of the migrant principle has proved to be effective in reducing the impact of the initial populations of genetic algorithms in optimising simple linear functions. Analytical and empirical results have also suggested that the method could be applied to locate an optimal solution in larger search space with more complex landscape. In the paper, an attempt has been made to develop an enhanced object matching technique that is based on the integration of the migrant principle and an existing genetic algorithm for affine invariant object recognition. As the latter had been taken as the foundation of a series of research works, any improvement on the scheme will directly benefit subsequent developments. The problem being addressed is highly nonlinear, which requires well-formed initial populations to attain successful matching of object shapes. Experimental results reveal that, for the same population size and mutation rate, the proposed method demonstrates significant improvement, as compared with its precedent, and that it is insensitive to the initial population.
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A relation between recursive object types, called matching, has been proposed [8] to provide an adequate typing of inheritance in class-based languages. This paper investigates the role of this relation in the design of a type sys...
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A relation between recursive object types, called matching, has been proposed [8] to provide an adequate typing of inheritance in class-based languages. This paper investigates the role of this relation in the design of a type system for the Lambda Calculus of Objects[15]. A new type system for this calculus is defined that uses implicit match-bounded quantification over type variables instead of implicit quantification over row schemes -as in [15] - to capture MyType polymorphic types for methods. An operational semantics is defined for the untyped calculus and type soundness for the new system is proved as a corollary of a subject reduction property. A formal analysis of the relative expressive power of the two systems is also carried out, that explains how the row schemes of [15] can be understood in terms of matching, and shows that the new system is as powerful as the original one on derivations of typing judgements for closed objects.
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The goal of cross-domain object matching (CDOM) is to find correspondence between two sets of objects in different domains in an unsupervised way. Photo album summarization is a typical application of CDOM, where photos are auto...
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The goal of cross-domain object matching (CDOM) is to find correspondence between two sets of objects in different domains in an unsupervised way. Photo album summarization is a typical application of CDOM, where photos are automatically aligned into a designed frame expressed in the Cartesian coordinate system. CDOM is usually formulated as finding a mapping from objects in one domain (photos) to objects in the other domain (frame) so that the pairwise dependency is maximized. A state-of-the-art CDOM method employs a kernel-based dependency measure, but it has a drawback that the kernel parameter needs to be determined manually. In this paper, we propose alternative CDOM methods that can naturally address the model selection problem. Through experiments on image matching, unpaired voice conversion, and photo album summarization tasks, the effectiveness of the proposed methods is demonstrated.
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The goal of cross-domain object matching (CDOM) is to find correspondence between two sets of objects in different domains in an unsupervised way. Photo album summarization is a typical application of CDOM, where photos are automa...
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The goal of cross-domain object matching (CDOM) is to find correspondence between two sets of objects in different domains in an unsupervised way. Photo album summarization is a typical application of CDOM, where photos are automatically aligned into a designed frame expressed in the Cartesian coordinate system. CDOM is usually formulated as finding a mapping from objects in one domain (photos) to objects in the other domain (frame) so that the pairwise dependency is maximized. A state-of-the-art CDOM method employs a kernel-based dependency measure, but it has a drawback that the kernel parameter needs to be determined manually. In this paper, we propose alternative CDOM methods that can naturally address the model selection problem. Through experiments on image matching, unpaired voice conversion, and photo album summarization tasks, the effectiveness of the proposed methods is demonstrated.
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Both the Method for Object-based Diagnostic Evaluation (MODE) and contiguous rain area (CRA) object-based verification techniques have been used to analyze precipitation forecasts from two sets of ensembles to determine if spread-...
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Both the Method for Object-based Diagnostic Evaluation (MODE) and contiguous rain area (CRA) object-based verification techniques have been used to analyze precipitation forecasts from two sets of ensembles to determine if spread-skill behavior observed using traditional measures can be seen in the object parameters. One set consisted of two eight-member Weather Research and Forecasting (WRF) model ensembles: one having mixed physics and dynamics with unperturbed initial and lateral boundary conditions (Phys) and another using common physics and a dynamic core but with perturbed initial and lateral boundary conditions (IC/LBC). Traditional measures found that spread grows much faster in IC/LBC than in Phys so that after roughly 24 h, better skill and spread are found in IC/LBC. These measures also reflected a strong diurnal signal of precipitation. The other set of ensembles included five members of a 4-km grid-spacing WRF ensemble (ENS4) and five members of a 20-km WRF ensemble (ENS20). Traditional measures suggested that the diurnal signal was better in ENS4 and spread increased more rapidly than in ENS20. Standard deviations (SDs) of four object parameters computed for the first set of ensembles using MODE and CRA showed the trend of enhanced spread growth in IC/LBC compared to Phys that had been observed in traditional measures, with the areal coverage of precipitation exhibiting the greatest growth in spread with time. The two techniques did not produce identical results; although, they did show the same general trends. A diurnal signal could be seen in the SDs of all parameters, especially rain rate, volume, and areal coverage. MODE results also found evidence of a diurnal signal and faster growth of spread in object parameters in ENS4 than in ENS20. Some forecasting approaches based on MODE and CRA output are also demonstrated. Forecasts based on averages of object parameters from each ensemble member were more skillful than forecasts based on MODE or CRA applied to an ensemble mean computed using the probability matching technique for areal coverage and volume, but differences in the two techniques were less pronounced for rain rate and displacement. The use of a probability threshold to define objects was also shown to be a valid forecasting approach with MODE.
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The object representation and tracking is one of the important tasks in computer vision. The object can be represented in various ways and in this paper the objects are represented using the properties of the HSV color space. Adap...
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The object representation and tracking is one of the important tasks in computer vision. The object can be represented in various ways and in this paper the objects are represented using the properties of the HSV color space. Adaptive k-means clustering algorithm was applied to cluster objects centroids color values and co-ordinates were sent to next frame for clustering. After clustering, for comparing the objects present in both the reference frame and the target frame, a similarity measure was proposed which uses position, color and size of the objects for comparison. Based on the similarity value, the objects were detected and tracked. The performance of the proposed approach was verified with human objects and the same was effectively tracked. The comparison was carried with similar methods and the results are encouraging.
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