Improved Camshift object tracking algorithm in occluded scenes based on AKAZE and Kalman

Pei, Lili; Zhang, He; Yang, Bo

doi:10.1007/s11042-021-11673-7

Cited by 6 publications

(1 citation statement)

References 26 publications

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“…The main algorithms that use nonlinear filtering function to construct scale space include the KAZE algorithm (Khalid et al, 2021 ; Liu J.-B. et al, 2021 ; Roy et al, 2022 ), AKAZE algorithm (Sharma and Jain, 2020 ; Ji et al, 2021 ; Pei et al, 2021 ; Yan et al, 2021 ), etc. The scale space constructed by the nonlinear filtering function can better protect the information at the edge of the image and increase the robustness of the matching algorithm.…”

Section: Introductionmentioning

confidence: 99%

High precision visual localization method of UAV based on feature matching

Xue¹,

Zhong²,

Liao³

et al. 2022

Front. Comput. Neurosci.

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In this paper, the precision hovering problem of UAV operation is studied. Aiming at the diversity and complexity of the UAV operating environment, a high-precision visual positioning and orientation method based on image feature matching was proposed. The image feature matching based on the improved AKAZE algorithm is realized, and the optimal matching point pair screening method based on the fusion of Hamming distance and matching line angle is innovatively proposed, which greatly improves the robustness of the algorithm without affecting the performance of the algorithm. The real-time image is matched with the benchmark image for image feature matching. By reducing the deviation of image feature, the pose state correction of UAV hovering is achieved, and the precision hovering of the UAV is realized. Both simulation and real UAV tests verify the effectiveness of the proposed UAV high-precision visual positioning and orientation method.

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Section: Introductionmentioning

confidence: 99%

High precision visual localization method of UAV based on feature matching

Xue¹,

Zhong²,

Liao³

et al. 2022

Front. Comput. Neurosci.

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3D reconstruction system and multiobject local tracking algorithm designed for billiards

et al. 2023

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The use of virtual reality or augmented reality systems in billiards sports are useful tools for pure entertainment or improving the player’s skills. Depending on the purpose of these systems, tracking algorithms based on computer vision must be used. These algorithms are especially useful in systems aiming to reconstruct the trajectories followed by the balls after a strike. However, depending on the billiard modality, the problem of tracking multiple small identical objects, such as balls, is a complex task. In addition, when an amateur or nontop professional player uses low-frame-rate and low-resolution devices, problems such as blurred balls, blurred contours, or fuzzy edges, among others, arise. These effects have a negative impact on ball-tracking accuracy and reconstruction quality. Thus, this work proposes two contributions. The first contribution is a new tracking algorithm called “multiobject local tracking (MOLT)”. This algorithm can track balls with high precision and accuracy even with motion blur caused by low-resolution and low-frame-rate devices. Moreover, the proposed MOLT algorithm is compared with nine tracking methods and four different metrics, outperforming the rest of the methods in the majority of the cases and providing a robust solution. The second contribution is a whole system to track (using the MOLT algorithm) and reconstruct the movements of the balls on a billiard table in a 3D virtual world using computer vision. The proposed system covers all steps from image capture to 3D reconstruction. The 3D reconstruction results have been qualitatively evaluated by different users through a series of questionnaires, obtaining an overall score of 7.6 (out of 10), which indicates that the system is a promising and useful tool for training. Finally, both the MOLT algorithm and the reconstruction system are tested in three billiard modalities: blackball, carom billiards, and snooker.

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