Laser Ranging-Assisted Binocular Visual Sensor Tracking System

Wang, Qilong; Yu, Zhengtao; Shi, Wenzhong; Nie, Meng

doi:10.3390/s20030688

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…In the process of selecting color features, a discriminant color descriptor (DD) is used to obtain stronger model tracking performance. After fusing hog and DD features, a discriminant color feature hog correlation filter (dhcf) is proposed [16]. DD can freely select the required dimensions.…”

Section: Kernel Correlation Filter Tracking Algorithmmentioning

confidence: 99%

Application of Correlation Filter Tracking Algorithm Based on Multiple Color Features in Basketball Motion Capture System

Quan

2022

Journal of Sensors

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The application of motion capture system in basketball game can intercept the highlights of the game and improve the viewing effect. In order to improve the tracking performance of target tracking algorithm and the efficiency of traditional basketball tracking system, a correlation filtering algorithm combining multiple color features is adopted in this study. A dhcf as algorithm based on multicolor feature is established. The adaptability of traditional KCF algorithm in scene attributes such as rapid target change and illumination change is mainly studied. The success rate of multicolor tracking algorithm can reach 78.2%. At the same time, based on the traditional kernel correlation filter, the algorithm combines the discriminant color descriptor and the direction gradient histogram. The results show that the success rate and accuracy of the algorithm in otb50 are 84.2% and 82.5%, respectively. In addition, excellent tracking performance is reflected in various scene attributes. Therefore, the algorithm can improve the target tracking performance of the traditional kernel correlation filter, especially solving the adaptability of the traditional algorithm in the scene of light change, fast target movement, and so on.

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Section: Kernel Correlation Filter Tracking Algorithmmentioning

confidence: 99%

Application of Correlation Filter Tracking Algorithm Based on Multiple Color Features in Basketball Motion Capture System

Quan

2022

Journal of Sensors

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“…As shown in the principle of using the four-point calibration method. The fixed laser ranging sensor [13] 1 is read as L . The TCP coordinate value of the point is calibrated as ( )…”

Section: D Online Measurement Algorithm For Workpiecementioning

confidence: 99%

Realization of calibration control algorithm for curved surface-oriented machine vision

Zhang

Jiao³

et al. 2022

J. Phys.: Conf. Ser.

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Aiming at the problem of the positioning deviation of the composite material surface metallization spraying equipment when spraying the workpiece, in order to ensure the processing quality, the robot vision calibration control algorithm is designed to complete the positioning calibration during the thermal spraying process. The vision calibration system is composed of a control system, a robot system, a vision system and a laser rangefinder. When the system is started, the laser rangefinder transmits the measurement data to the control system in real time, and the XYZABC position compensation data is processed by the control algorithm and transmitted to the robot to complete the vision calibration. Take composite material surface metallization automatic spraying equipment as an example, the result is that the robot system’s planar motion radius is greater than or equal to 2.55m, the height direction motion distance is 3m, the repeat positioning accuracy is ±0.05mm, and the mold positioning accuracy is ±0.08mm. Finally, the composite material surface metal is completed. Chemical spraying operations.

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“…In engineering applications, sensor information fusion refers to the integration and processing of sensor information from different sources and modes according to certain algorithms and strategies in order to describe the sensed objects accurately and reasonably [72]. Information fusion can enhance the availability of data and reduce fuzziness; additionally, it can increase the coverage of time and space [73].Therefore, based on a binocular camera, CMRRs can be equipped with auxiliary sensors such as lidar sensors, infrared sensors or inertial measurement units [74], [75], [76], [77], [78]. Then, the multidimensional information can be integrated, which can effectively improve the robustness, adaptability, and fault tolerance of the robot's perception system, more accurately obtain the key information of the accident site, and provide a good foundation for the path planning and autonomous decision-making of the robot.…”

Section: ) Multisensor Information Fusionmentioning

confidence: 99%

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

Zhai

Zhang

et al. 2020

IEEE Access

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Rescue work after a coal mine accident is fraught with challenges and dangers. Considering the safety of rescue workers and the urgency of a rescue mission, it is necessary to use coal mine rescue robots to perform the tasks of environmental detection and rescue. As a key part of the robot sensing system, a visual sensor can provide much information about a scene. Among vision sensor types, binocular vision has the advantages of being noncontact and passive, and it is the key technology for a robot to acquire obstacle information and reconstruct a three-dimensional scene. Therefore, coal mine rescue robots based on binocular vision have become a popular research topic in the field of mine safety. First, the research status of camera calibration and stereo vision matching for binocular vision is systematically introduced in this paper. Second, the latest research progress on coal mine rescue robots based on binocular vision is reviewed from the perspective of technological applications and development. Finally, the technical challenges and future development trends of binocular vision in coal mine rescue robots are described. INDEX TERMS Coal mine rescue robots, binocular vision, camera calibration, stereo vision matching.

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Laser Ranging-Assisted Binocular Visual Sensor Tracking System

Cited by 9 publications

References 21 publications

Application of Correlation Filter Tracking Algorithm Based on Multiple Color Features in Basketball Motion Capture System

Application of Correlation Filter Tracking Algorithm Based on Multiple Color Features in Basketball Motion Capture System

Realization of calibration control algorithm for curved surface-oriented machine vision

Coal Mine Rescue Robots Based on Binocular Vision: A Review of the State of the Art

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