The Experts below are selected from a list of 18240 Experts worldwide ranked by ideXlab platform
Sarfraz Khurshid - One of the best experts on this subject based on the ideXlab platform.
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an empirical study of Structural Constraint solving techniques
International Conference on Formal Engineering Methods, 2009Co-Authors: Junaid Haroon Siddiqui, Sarfraz KhurshidAbstract:Structural Constraint solving allows finding object graphs that satisfy given Constraints, thereby enabling software reliability tasks, such as systematic testing and error recovery. Since enumerating all possible object graphs is prohibitively expensive, researchers have proposed a number of techniques for reducing the number of potential object graphs to consider as candidate solutions. These techniques analyze the Structural Constraints to prune from search object graphs that cannot satisfy the Constraints. Although, analytical and empirical evaluations of individual techniques have been done, comparative studies of different kinds of techniques are rare in the literature. We performed an experiment to evaluate the relative strengths and weaknesses of some key Structural Constraint solving techniques. The experiment considered four techniques using: a model checker, a SAT solver, a symbolic execution engine, and a specialized solver. It focussed on their relative abilities in expressing the Constraints and formatting the output object graphs, and most importantly on their performance. Our results highlight the tradeoffs of different techniques and help choose a technique for practical use.
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ICFEM - An Empirical Study of Structural Constraint Solving Techniques
Formal Methods and Software Engineering, 2009Co-Authors: Junaid Haroon Siddiqui, Sarfraz KhurshidAbstract:Structural Constraint solving allows finding object graphs that satisfy given Constraints, thereby enabling software reliability tasks, such as systematic testing and error recovery. Since enumerating all possible object graphs is prohibitively expensive, researchers have proposed a number of techniques for reducing the number of potential object graphs to consider as candidate solutions. These techniques analyze the Structural Constraints to prune from search object graphs that cannot satisfy the Constraints. Although, analytical and empirical evaluations of individual techniques have been done, comparative studies of different kinds of techniques are rare in the literature. We performed an experiment to evaluate the relative strengths and weaknesses of some key Structural Constraint solving techniques. The experiment considered four techniques using: a model checker, a SAT solver, a symbolic execution engine, and a specialized solver. It focussed on their relative abilities in expressing the Constraints and formatting the output object graphs, and most importantly on their performance. Our results highlight the tradeoffs of different techniques and help choose a technique for practical use.
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ASE - Optimizing a Structural Constraint Solver for Efficient Software Checking
2009 IEEE ACM International Conference on Automated Software Engineering, 2009Co-Authors: Junaid Haroon Siddiqui, Darko Marinov, Sarfraz KhurshidAbstract:Several static analysis techniques, e.g., symbolic execution or scope-bounded checking, as well as dynamic analysis techniques, e.g., specification-based testing, use Constraint solvers as an enabling technology. To analyze code that manipulates Structurally complex data, the underlying solver must support Structural Constraints. Solving such Constraints can be expensive due to the large number of aliasing possibilities that the solver must consider. This paper presents a novel technique to selectively reduce the number of test cases to be generated. Our technique applies across a class of Structural Constraint solvers. Experimental results show that the technique enables an order of magnitude reduction in the number of test cases to be considered.
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Optimizing a Structural Constraint Solver for Efficient Software Checking
2009 IEEE ACM International Conference on Automated Software Engineering, 2009Co-Authors: Junaid Haroon Siddiqui, Darko Marinov, Sarfraz KhurshidAbstract:Several static analysis techniques, e.g., symbolic execution or scope-bounded checking, as well as dynamic analysis techniques, e.g., specification-based testing, use Constraint solvers as an enabling technology. To analyze code that manipulates Structurally complex data, the underlying solver must support Structural Constraints. Solving such Constraints can be expensive due to the large number of aliasing possibilities that the solver must consider. This paper presents a novel technique to selectively reduce the number of test cases to be generated. Our technique applies across a class of Structural Constraint solvers. Experimental results show that the technique enables an order of magnitude reduction in the number of test cases to be considered.
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ISSTA - Efficient solving of Structural Constraints
Proceedings of the 2008 international symposium on Software testing and analysis - ISSTA '08, 2008Co-Authors: Bassem Elkarablieh, Darko Marinov, Sarfraz KhurshidAbstract:Structural Constraint solving is being increasingly used for software reliability tasks such as systematic testing or error recovery. For example, the Korat algorithm provides Constraint-based test generation: given a Java predicate that describes desired input Constraints and a bound on the input size, Korat systematically searches the bounded input space of the predicate to generate all inputs that satisfy the Constraints. As another example, the STARC tool uses a Constraint-based search to repair broken data structures. A key issue for these approaches is the efficiency of search. This paper presents a novel approach that significantly improves the efficiency of Structural Constraint solvers. Specifically, most existing approaches use backtracking through code re-execution to explore their search space. In contrast, our approach performs checkpoint-based backtracking by storing partial program states and performing abstract undo operations. The heart of our approach is a light-weight search that is performed purely through code instrumentation. The experimental results on Korat and STARC for generating and repairing a set of complex data structures show an order to two orders of magnitude speed-up over the traditionally used searches.
Kuk-jin Yoon - One of the best experts on this subject based on the ideXlab platform.
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Structural Constraint data association for online multi object tracking
International Journal of Computer Vision, 2019Co-Authors: Ju Hong Yoon, Ming-hsuan Yang, Kuk-jin YoonAbstract:Online two-dimensional (2D) multi-object tracking (MOT) is a challenging task when the objects of interest have similar appearances. In that case, the motion of objects is another helpful cue for tracking and discriminating multiple objects. However, when using a single moving camera for online 2D MOT, observable motion cues are contaminated by global camera movements and, thus, are not always predictable. To deal with unexpected camera motion, we propose a new data association method that effectively exploits Structural Constraints in the presence of large camera motion. In addition, to reduce incorrect associations with mis-detections and false positives, we develop a novel event aggregation method to integrate assignment costs computed by Structural Constraints. We also utilize Structural Constraints to track missing objects when they are re-detected again. By doing this, identities of the missing objects can be retained continuously. Experimental results validated the effectiveness of the proposed data association algorithm under unexpected camera motions. In addition, tracking results on a large number of benchmark datasets demonstrated that the proposed MOT algorithm performs robustly and favorably against various online methods in terms of several quantitative metrics, and that its performance is comparable to offline methods.
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Online Multi-object Tracking via Structural Constraint Event Aggregation
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016Co-Authors: Ju Hong Yoon, Ming-hsuan Yang, Kuk-jin YoonAbstract:Multi-object tracking (MOT) becomes more challenging when objects of interest have similar appearances. In that case, the motion cues are particularly useful for discriminating multiple objects. However, for online 2D MOT in scenes acquired from moving cameras, observable motion cues are complicated by global camera movements and thus not always smooth or predictable. To deal with such unexpected camera motion for online 2D MOT, a Structural motion Constraint between objects has been utilized thanks to its robustness to camera motion. In this paper, we propose a new data association method that effectively exploits Structural motion Constraints in the presence of large camera motion. In addition, to further improve the robustness of data association against mis-detections and false positives, a novel event aggregation approach is developed to integrate Structural Constraints in assignment costs for online MOT. Experimental results on a large number of datasets demonstrate the effectiveness of the proposed algorithm for online 2D MOT.
Ju Hong Yoon - One of the best experts on this subject based on the ideXlab platform.
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Structural Constraint data association for online multi object tracking
International Journal of Computer Vision, 2019Co-Authors: Ju Hong Yoon, Ming-hsuan Yang, Kuk-jin YoonAbstract:Online two-dimensional (2D) multi-object tracking (MOT) is a challenging task when the objects of interest have similar appearances. In that case, the motion of objects is another helpful cue for tracking and discriminating multiple objects. However, when using a single moving camera for online 2D MOT, observable motion cues are contaminated by global camera movements and, thus, are not always predictable. To deal with unexpected camera motion, we propose a new data association method that effectively exploits Structural Constraints in the presence of large camera motion. In addition, to reduce incorrect associations with mis-detections and false positives, we develop a novel event aggregation method to integrate assignment costs computed by Structural Constraints. We also utilize Structural Constraints to track missing objects when they are re-detected again. By doing this, identities of the missing objects can be retained continuously. Experimental results validated the effectiveness of the proposed data association algorithm under unexpected camera motions. In addition, tracking results on a large number of benchmark datasets demonstrated that the proposed MOT algorithm performs robustly and favorably against various online methods in terms of several quantitative metrics, and that its performance is comparable to offline methods.
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Online Multi-object Tracking via Structural Constraint Event Aggregation
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016Co-Authors: Ju Hong Yoon, Ming-hsuan Yang, Kuk-jin YoonAbstract:Multi-object tracking (MOT) becomes more challenging when objects of interest have similar appearances. In that case, the motion cues are particularly useful for discriminating multiple objects. However, for online 2D MOT in scenes acquired from moving cameras, observable motion cues are complicated by global camera movements and thus not always smooth or predictable. To deal with such unexpected camera motion for online 2D MOT, a Structural motion Constraint between objects has been utilized thanks to its robustness to camera motion. In this paper, we propose a new data association method that effectively exploits Structural motion Constraints in the presence of large camera motion. In addition, to further improve the robustness of data association against mis-detections and false positives, a novel event aggregation approach is developed to integrate Structural Constraints in assignment costs for online MOT. Experimental results on a large number of datasets demonstrate the effectiveness of the proposed algorithm for online 2D MOT.
Qingxiong Yang - One of the best experts on this subject based on the ideXlab platform.
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Real-time part-based visual tracking via adaptive correlation filters
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015Co-Authors: Gang Wang, Qingxiong YangAbstract:Robust object tracking is a challenging task in computer vision. To better solve the partial occlusion issue, part-based methods are widely used in visual object trackers. However, due to the complicated online training and updating process, most of these part-based trackers cannot run in real-time. Correlation filters have been used in tracking tasks recently because of the high efficiency. However, the conventional correlation filter based trackers cannot deal with occlusion. Furthermore, most correlation filter based trackers fix the scale and rotation of the target which makes the trackers unreliable in long-term tracking tasks. In this paper, we propose a novel tracking method which track objects based on parts with multiple correlation filters. Our method can run in real-time. Additionally, the Bayesian inference framework and a Structural Constraint mask are adopted to enable our tracker to be robust to various appearance changes. Extensive experiments have been done to prove the effectiveness of our method.
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Part-based Tracking via Discriminative Correlation Filters
IEEE Transactions on Circuits and Systems for Video Technology, 1Co-Authors: Gang Wang, Qingxiong Yang, Li WangAbstract:In order to better deal with the partial occlusion issue, part-based trackers are widely used in visual object tracking recently. However, it is still difficult to realize fast and robust tracking, due to complicated online training and updating process. Correlation filters have been used in visual object tracking tasks recently because of their high efficiency. However, the traditional correlation filter based tracking methods do not deal with occlusion well. In this paper, we propose a novel tracking method which tracks objects based on parts with multiple correlation filters. The Bayesian inference framework and a Structural Constraint mask are adopted to enable our tracker to be robust to various appearance changes. Additionally, a discriminative part selection scheme is adopted to further improve performance and accelerate our method. Experimental results demonstrate that our multiple part tracker can significantly improve tracking performance on benchmark datasets.
Gang Wang - One of the best experts on this subject based on the ideXlab platform.
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Real-time part-based visual tracking via adaptive correlation filters
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015Co-Authors: Gang Wang, Qingxiong YangAbstract:Robust object tracking is a challenging task in computer vision. To better solve the partial occlusion issue, part-based methods are widely used in visual object trackers. However, due to the complicated online training and updating process, most of these part-based trackers cannot run in real-time. Correlation filters have been used in tracking tasks recently because of the high efficiency. However, the conventional correlation filter based trackers cannot deal with occlusion. Furthermore, most correlation filter based trackers fix the scale and rotation of the target which makes the trackers unreliable in long-term tracking tasks. In this paper, we propose a novel tracking method which track objects based on parts with multiple correlation filters. Our method can run in real-time. Additionally, the Bayesian inference framework and a Structural Constraint mask are adopted to enable our tracker to be robust to various appearance changes. Extensive experiments have been done to prove the effectiveness of our method.
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Part-based Tracking via Discriminative Correlation Filters
IEEE Transactions on Circuits and Systems for Video Technology, 1Co-Authors: Gang Wang, Qingxiong Yang, Li WangAbstract:In order to better deal with the partial occlusion issue, part-based trackers are widely used in visual object tracking recently. However, it is still difficult to realize fast and robust tracking, due to complicated online training and updating process. Correlation filters have been used in visual object tracking tasks recently because of their high efficiency. However, the traditional correlation filter based tracking methods do not deal with occlusion well. In this paper, we propose a novel tracking method which tracks objects based on parts with multiple correlation filters. The Bayesian inference framework and a Structural Constraint mask are adopted to enable our tracker to be robust to various appearance changes. Additionally, a discriminative part selection scheme is adopted to further improve performance and accelerate our method. Experimental results demonstrate that our multiple part tracker can significantly improve tracking performance on benchmark datasets.
