摘要 :
An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement ...
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An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement focuses on two aspects, one is adaption, which makes the algorithm not dependent on the pre-defined scanning grids by online generating scale space, and the other is efficiency, which uses not only algorithm-level acceleration like scale prediction that employs auto-regression and moving average (ARMA) model to learn the object motion to lessen the detector's searching range and the fixed number of positive and negative samples that ensures a constant retrieving time, but also CPU and GPU parallel technology to achieve hardware acceleration. In addition, in order to obtain a better effect, some TLD's details are redesigned, which uses a weight including both normalized correlation coefficient and scale size to integrate results, and adjusts distance metric thresholds online. A contrastive experiment on success rate, center location error and execution time, is carried out to show a performance and efficiency upgrade over state-of-the-art TLD with partial TLD datasets and Shenzhou IX return capsule image sequences. The algorithm can be used in the field of video surveillance to meet the need of real-time video tracking.
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摘要 :
An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement ...
展开
An improved online long-term visual tracking algorithm, named adaptive and accelerated TLD (AA-TLD) based on Tracking-Learning-Detection (TLD) which is a novel tracking framework has been introduced in this paper. The improvement focuses on two aspects, one is adaption, which makes the algorithm not dependent on the pre-defined scanning grids by online generating scale space, and the other is efficiency, which uses not only algorithm-level acceleration like scale prediction that employs auto-regression and moving average (ARMA) model to learn the object motion to lessen the detector's searching range and the fixed number of positive and negative samples that ensures a constant retrieving time, but also CPU and GPU parallel technology to achieve hardware acceleration. In addition, in order to obtain a better effect, some TLD's details are redesigned, which uses a weight including both normalized correlation coefficient and scale size to integrate results, and adjusts distance metric thresholds online. A contrastive experiment on success rate, center location error and execution time, is carried out to show a performance and efficiency upgrade over state-of-the-art TLD with partial TLD datasets and Shenzhou IX return capsule image sequences. The algorithm can be used in the field of video surveillance to meet the need of real-time video tracking.
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摘要 :
Camera calibration is one of the most basic and important processes in optical measuring field. Generally, the objective of camera calibration is to estimate the internal and external parameters of object cameras, while the orient...
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Camera calibration is one of the most basic and important processes in optical measuring field. Generally, the objective of camera calibration is to estimate the internal and external parameters of object cameras, while the orientation error of optical axis is not included yet. Orientation error of optical axis is a important factor, which seriously affects measuring precision in high-precision measurement field, especially for those distant aerospace measurement in which object distance is much longer than focal length, that lead to magnifying the orientation errors to thousands times. In order to eliminate the influence of orientation error of camera optical axis, the imaging model of camera is analysed and established in this paper, and the calibration method is also introduced: Firstly, we analyse the reasons that cause optical axis error and its influence. Then, we find the model of optical axis orientation error and imaging model of camera basing on it's practical physical meaning. Furthermore, we derive the bundle adjustment algorithm which could compute the internal and external camera parameters and absolute orientation of camera optical axis simultaneously at high precision. In numeric simulation, we solve the camera parameters by using bundle adjustment optimization algorithm, then we correct the image points by calibration results according to the model of optical axis error, and the simulation result shows that our calibration model is reliable, effective and precise.
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摘要 :
Camera calibration is one of the most basic and important processes in optical measuring field. Generally, the objective of camera calibration is to estimate the internal and external parameters of object cameras, while the orient...
展开
Camera calibration is one of the most basic and important processes in optical measuring field. Generally, the objective of camera calibration is to estimate the internal and external parameters of object cameras, while the orientation error of optical axis is not included yet. Orientation error of optical axis is a important factor, which seriously affects measuring precision in high-precision measurement field, especially for those distant aerospace measurement in which object distance is much longer than focal length, that lead to magnifying the orientation errors to thousands times. In order to eliminate the influence of orientation error of camera optical axis, the imaging model of camera is analysed and established in this paper, and the calibration method is also introduced: Firstly, we analyse the reasons that cause optical axis error and its influence. Then, we find the model of optical axis orientation error and imaging model of camera basing on it's practical physical meaning. Furthermore, we derive the bundle adjustment algorithm which could compute the internal and external camera parameters and absolute orientation of camera optical axis simultaneously at high precision. In numeric simulation, we solve the camera parameters by using bundle adjustment optimization algorithm, then we correct the image points by calibration results according to the model of optical axis error, and the simulation result shows that our calibration model is reliable, effective and precise.
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