Performance Measurement of Photoelectric Detection and Target Tracking Algorithm

Yang, Haidou; Li, Wei

doi:10.21307/ijssis-2017-819

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…The photoelectric tracking system is dependent on the image data for target recognition and tracking; hence, image signal processing is critical to the system. The photoelectric tracking system's image signal processing presents significant challenges in complicated settings with factors including object occlusion, ultra-long distance, inadequate brightness, and complex backgrounds [15].…”

Section: Introductionmentioning

confidence: 99%

Special Issue on Advances in Photoelectric Tracking Systems: An Overview

Deng,

Bao,

Wang

et al. 2024

Photonics

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Section: Introductionmentioning

confidence: 99%

Special Issue on Advances in Photoelectric Tracking Systems: An Overview

Deng,

Bao,

Wang

et al. 2024

Photonics

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“…With the development of information technology, there have been widely applications of computer vision such as video surveillance, human computer interaction and video conference [1][2].Extracting moving foreground objects from a video sequence is the first critical step in video analytics [3][4]. The most widely used approach of moving object detection is background subtraction.…”

Section: Introductionmentioning

confidence: 99%

Detection Of Moving Object By Fusion Of Color And Depth Information

Zhang

Zhao

Liu

2016

International Journal on Smart Sensing and Intelligent Systems

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Moving object detection based on color information is easily affected by illumination changes and shadows in complex scenes. Depth information can provide complementary information. In the paper, a novel method is presented by using color and depth information. Firstly, we improve the codebook algorithm by fusing the depth information as the fourth channel in the code word. Next, a compensation factor algorithm is presented to make the edges accurate. So the final detection result can be obtained by logic operation. Experiments adapt the public datasets, and experimental results show that the proposed method can successfully cope with the limitations of color-based or depth-based detection.

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“…In recent years, the UAV is widely used in military, agriculture, life and other aspects. UAV geotargeting technology, one of the important missions of UAV, is the foundation of targeting tracking [1] and surveillance technique, and the positioning accuracy will directly affect the follow-up tasks. However, due to some objective limits, such as installation errors, lower precision of measurement unit, there often exist errors in the sensors of UAV, which deleteriously affects the target positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

High Precision Target Localization Method Based On Compensation Of Attitude Angle Errors

Ding²,

2016

International Journal on Smart Sensing and Intelligent Systems

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Abstract-The attitude angles of UAV, as the input parameters of the target localization process, influence the accuracy of geo-targeting. In order to improve the accuracy of target localization, this paper compensates the attitude angle errors of the UAV based on learning prediction compensation. Firstly, considering the airborne equipment and the metadata provided by the UAV, we combine rear intersection with GPS/INS to calculate the error of each platform and aircraft attitude angle. Then the error prediction model to compensate each platform and aircraft attitude error is derived by analyzing the error distribution and polynomial regression. Afterwards, because of the limit of the UAV aerial image amount and the similar influence of each attitude angle error on targeting and geometric correction, we use equivalent optical axis angle to represent platform and aircraft attitudes. Furthermore, we also predict and compensate the error of the equivalent angle. In this process, we adopt SVM and regression to classify and obtain error prediction model of equivalent optical axis angle. Finally, the actual data is used to verify the compensation algorithm. The results show that the method can improve the accuracy of target localization efficiently, and has a certain value of engineering guidance and practical application.

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Performance Measurement of Photoelectric Detection and Target Tracking Algorithm

Cited by 4 publications

References 14 publications

Special Issue on Advances in Photoelectric Tracking Systems: An Overview

Special Issue on Advances in Photoelectric Tracking Systems: An Overview

Detection Of Moving Object By Fusion Of Color And Depth Information

High Precision Target Localization Method Based On Compensation Of Attitude Angle Errors

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