Abstract
This paper proposes a moving-target tracking algorithm that measures the pose of a micro-robot with high precision and high speed using the Kalman filter-based kernelized correlation filter (K2CF) algorithm. The adaptive Kalman filter can predict the state of linearly and nonlinearly fast-moving targets. The kernelized correlation filter algorithm then accurately detects the positions of the moving targets and uses the detection results to modify the moving states of the targets. This paper verifies the performance of the algorithm on a monocular vision measurement platform and using a pose measurement method. The K2CF algorithm was embedded in the micro-robot’s attitude measurement system, and the tracking performances of three different trackers were compared under different motion conditions. Our tracker improved the positioning accuracy and maintained real-time operation. In a comparison study of K2CF and many other algorithms on Object Tracking Benchmark-50 and Object Tracking Benchmark-100 video sequences, the K2CF algorithm achieved the highest accuracy. In the 400 mm × 300 mm field of view, when the target radius is about 3 mm and the inter-frame acceleration displacement does not exceed 5.6 mm, the root-mean-square error of position and attitude angle can satisfy the precision requirements of the system.
Highlights
Technological developments in information, electronics and mechatronics have advanced the use of micro-robots in precision operation fields, such as complex assembly [1,2], advanced machining [3,4], intelligent manufacturing [5], automatic monitoring [6,7], nondestructive testing [8,9] and digital printing [10,11]
When the tracker encounters an occlusion, the Kalman filter omits the observed values of the kernelized correlation filter (KCF) and updates the state based on the previous state
To correct the boundary effect in the KCF tracking algorithm, this paper proposes an adaptive kernelized correlation filter (K2 CF) algorithm that integrates the adaptive Kalman filter and can effectively predict the position of the target in different moving states
Summary
Technological developments in information, electronics and mechatronics have advanced the use of micro-robots in precision operation fields, such as complex assembly [1,2], advanced machining [3,4], intelligent manufacturing [5], automatic monitoring [6,7], nondestructive testing [8,9] and digital printing [10,11]. Production model tracking algorithms detect the target region by dense or sparse search of each frame image. The KCF in their method estimates the target position based on the Kalman filter prediction and updates the kernel model . When the tracker encounters an occlusion, the Kalman filter omits the observed values of the KCF and updates the state based on the previous state This tracker properly handles occlusion and human-crossing tasks, but its Kalman filter cannot effectively predict the target information of fast-moving objects. To correct the boundary effect in the KCF tracking algorithm, this paper proposes an adaptive kernelized correlation filter (K2 CF) algorithm that integrates the adaptive Kalman filter and can effectively predict the position of the target in different moving states. The image coordinates and motion coordinates are transformed by the camera calibration the feature points of the micro-robot in the field of view and acquire the posi direction angle of the moving target
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