The authors report the development of algorithms for the detection and tracking of object returns in noisy sector-scan sonar image sequences. Static objects are first removed using spatial and frequency domain filteri...
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The authors report the development of algorithms for the detection and tracking of object returns in noisy sector-scan sonar image sequences. Static objects are first removed using spatial and frequency domain filtering. The optical flow of the resulting images (containing only dynamic returns) is calculated. Significant dynamic returns are detected and segmented using adaptive thresholding. The average optical flow of each significant return is used by a tracking algorithm both to constrain search window radii and to derive a similarity measure. A tree of possible tracks is maintained in which the cumulative similarity measure is used to identify the most likely tracks and to prune out the least likely object sequences. The results of experiments using real scan sequences are presented to show the utility of the proposed tracking system.
tracking algorithms for IRST and radar are implemented and their performance is checked with simulated data. Detailed mathematical expressions given Could be useful for implementation. Performance evaluation metrics h...
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tracking algorithms for IRST and radar are implemented and their performance is checked with simulated data. Detailed mathematical expressions given Could be useful for implementation. Performance evaluation metrics have been presented to check the tracking algorithm performance. Two fusion schemes have been presented and their performances evaluated with simulated data. It is concluded that both fusion schemes performed alike with the second fusion scheme giving slightly better results. From the results, it is also concluded that fusion of IRST and radar would improve the tracking performance and reduce the positional uncertainty compared to individual trackers.
In recent years, tracking algorithms based on correlation filtering have been widely considered because of their high real-time performance. However, most of these algorithms do not consider the reliability of trackin...
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In recent years, tracking algorithms based on correlation filtering have been widely considered because of their high real-time performance. However, most of these algorithms do not consider the reliability of tracking results;thus, model drift is often a challenge in long-term target tracking. Moreover, the target is significantly occluded or disappears in long-term target tracking, hence the need for research to address such complexities. This paper proposes a long-term tracking framework comprising a tracking and re-detection module. The tracking module is based on the efficient convolution operator target-tracking algorithm (ECO) (Danelljan et al. in Proc IEEE Conf Comput Vis Pattern Recognit, 1). To redetect a lost target, a tracking uncertainty estimation method is developed that evaluates the tracking results of each image. Furthermore, an adaptive model-updating method is proposed, which can reduce the number of model updates and improve the robustness of the tracking algorithm. The model is inspired by long and short-term memory pool mechanisms of the brain, applying both mechanisms to the traditional tracking algorithm to improve long-term tracking. The memory model is effectively integrated into a brain-inspired visual long-term tracker through mutual learning and inspiration from computer and biological vision. In addition, the brain-inspired visual model can be made bio-cognitive on a small hardware platform with limited computing power. Central processing unit (CPU)-based experiments using two data sets, UAV20L (Mueller et al. in Eur Conf Comput Vision, 2) and UAV123 (Wu et al. in Proc IEEE Conf Comput Vis Pattern Recognit, 3), confirmed that the proposed method runs faster than 30fps. Furthermore, the long-term tracking test using the UAV20L data set showed that the proposed method performs better than any other method by 39%. Compared with conventional tracking methods, the proposed method has better performance in terms of coverage rate and position
The application of an auto-guided tractor to rice cultivation in Korean paddy fields may be limited by tire slippage and headland turning due to wet soil conditions and the use of small-sized fields 30%, thereby requ...
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The application of an auto-guided tractor to rice cultivation in Korean paddy fields may be limited by tire slippage and headland turning due to wet soil conditions and the use of small-sized fields < 1 ha. This study examines the development of a computer simulator capable of virtually testing the motion of a tractor based on a look-ahead distance method in a real-time 3D graphic environment. The tractor motion was simulated based on the dynamic model of a vehicle that considered the effects of tire slippage and side force imparted by soil. The validity of the computer simulation was confirmed by a paved road test, thereby providing tracking trajectories similar to those obtained from the road test. However, an oscillation in the steering angle, ranging from -8 to +5 deg., occurred when the tractor traveled on straight paths. This oscillation might be related to the limitations of the electric motor in controlling the tractor's hydraulic steering system due to the non-linear response of the hydraulic actuator to the rotation of the steering wheel by motor torque. In an arable field (90 m x 25 m), the auto-guided tractor followed the predefined path including C-shape headland turning with acceptable tracking, showing RMS lateral errors ranging from 3.8 to 12.8 cm on the straight paths. However, RMS lateral errors obtained on the curved paths increased to 100 cm when the tractor traveled in a wet sub-field with moisture content > 30%, thereby requiring more accurate estimation of sliding parameters. (C) 2015 Elsevier B.V. All rights reserved.
Recently, reentry vehicle (RV) tracking has become an important issue because of its high speed and high-elevation angle. The extended Kalman filter (EKF) with input estimation (IE) was developed for an RV tracking in...
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Recently, reentry vehicle (RV) tracking has become an important issue because of its high speed and high-elevation angle. The extended Kalman filter (EKF) with input estimation (IE) was developed for an RV tracking in a clear environment. However, radar tracking suffers from clutters in the form of unwanted, unavoidable and unpredictable signal echoes from sea, land and weather. The Hough transform is a well-known technique for identifying a straight line in noisy environments. This technique can be utilised to design a clutter elimination algorithm for clutters censoring. This study presents a novel tracking algorithm by integrating the EKF, IE and clutter elimination algorithm to track the RVs in cluttered environments. To ensure an RV to be tracked continuously, the estimation errors between the predicted and the real trajectories should be within a range cell. Simulation results reveal that the proposed algorithm can provide an acceptable accuracy to maintain the track. Although clutters cannot be totally censored, the effects of resting clutters can be reduced to an acceptable range. The proposed algorithm may thus help radars in achieving continuous tracking. In conclusion, this algorithm deserves further study and application.
In the last years, significant advances in microscopy techniques and the introduction of a novel technology to label living cells with genetically encoded fluorescent proteins revolutionized the field of Cell Biology....
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In the last years, significant advances in microscopy techniques and the introduction of a novel technology to label living cells with genetically encoded fluorescent proteins revolutionized the field of Cell Biology. Our understanding on cell dynamics built from snapshots on fixed specimens has evolved thanks to our actual capability to monitor in real time the evolution of processes in living cells. Among these new tools, single particle tracking techniques were developed to observe and follow individual particles. Hence, we are starting to unravel the mechanisms driving the motion of a wide variety of cellular components ranging from organelles to protein molecules by following their way through the cell. In this review, we introduce the single particle tracking technology to new users. We briefly describe the instrumentation and explain some of the algorithms commonly used to locate and track particles. Also, we present some common tools used to analyze trajectories and illustrate with some examples the applications of single particle tracking to study dynamics in living cells.
When related to a single and good contrast object or a laser spot, position sensing, or sensitive, detectors (PSDs) have a series of advantages over the classical camera sensors, including a good positioning accuracy ...
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When related to a single and good contrast object or a laser spot, position sensing, or sensitive, detectors (PSDs) have a series of advantages over the classical camera sensors, including a good positioning accuracy for a fast response time and very simple signal conditioning circuits. To test the performance of this kind of sensor for microrobotics, we have made a comparative analysis between a precise but slow video camera and a custom-made fast PSD system applied to the tracking of a diffuse-reflectivity object transported by a pneumatic microconveyor called Smart-Surface. Until now, the fast system dynamics prevented the full control of the smart surface by visual servoing, unless using a very expensive high frame rate camera. We have built and tested a custom and low cost PSD-based embedded circuit, optically connected with a camera to a single objective by means of a beam splitter. A stroboscopic light source enhanced the resolution. The obtained results showed a good linearity and a fast (over 500 frames per second) response time which will enable future closed-loop control by using PSD.
In order to improve vision tracking quality of the bionic robot, the new automatic tracking algorithm system is proposed in this paper. Base on design of FPGA image acquisition system, the scene noise is removed by ad...
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In order to improve vision tracking quality of the bionic robot, the new automatic tracking algorithm system is proposed in this paper. Base on design of FPGA image acquisition system, the scene noise is removed by adaptive wiener filtering. Aiming at the problem of ROI region extraction in the scene, the seed pixel is selected with background subtraction, orderly, the neighborhood point is judged, the label of the primary selection seed is calibrated. The scene image segmentation algorithm is proposed based on snake model. The matching process is to find the maximum optimization process of the similar function, and the gradient drop method is adopted in mean shift algorithm. Extended kalman filtering is used to realize the robustness state estimation and prediction of the target tracking system. The results given by tracing experiment indicate that the proposed detailed algorithm is effective for partial loss of maneuvering target.
A framework of automatic clustering and tracking algorithm is proposed for the multipath components (MPCs) in time-variant radio channels. The algorithm is based on the channel dynamics in time domain and is able to r...
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A framework of automatic clustering and tracking algorithm is proposed for the multipath components (MPCs) in time-variant radio channels. The algorithm is based on the channel dynamics in time domain and is able to reflect the birth and death behaviors of MPCs naturally. The proposed algorithm is validated by a ray-tracer and the spatial channel model extension simulations. Compared with other existing clustering algorithms, the proposed framework is able to cluster the timevarying MPCs and track the clusters with high accuracy and low complexity.
The main purpose of this paper is defining a procedure that allows the tracking of the measuring point in notched specimens under static testing, investigated by full field experimental methodologies. One of the main ...
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The main purpose of this paper is defining a procedure that allows the tracking of the measuring point in notched specimens under static testing, investigated by full field experimental methodologies. One of the main problems is the motion of the measuring zone, associated with its compliance under increasing load. A procedure based on Digital Image Correlation (D.I.C.) aiming to define the stress concentration factor in a dynamic way has been developed. Furthermore, numerous revealing tests based on Thermographic Analysis (T.A.) have been carried out on flat plastics specimens with different hole diameters. The assessed and final tracking procedure could be used to better define the trend of temperature changes during the static tensile test, as well as measuring the stress concentration factors in notched specimens. (C) 2017 Elsevier Ltd. All rights reserved.
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