Real-time Target Human Tracking using Camshift and LucasKanade Optical Flow Algorithm

Nguyen, Anh‐Tu; Nguyen, Van‐Truong; Bui, Huy-Anh; Nguyen, Xuan-Thuan

doi:10.25046/aj0602103

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…The equation ( 1) is solved by applying the singular-value Decomposition algorithm [33]. It is clear that at least four pairs of the points are utilized to calculate the matrix H. To find four pairs of points which describe the image features, the ORB feature and Brute-force matching method [29,30] is used. In particular, the ORB feature is adopted to search extracted key points from the two images.…”

Section: Fig 1 Flowchart Of the Proposed Algorithmmentioning

confidence: 99%

“…In light of the remarkable importance and advantages previously mentioned, a real-time defect detection system is explored to ensure the quality of the PCBs surface. The proposed method is designed by coupling the ORB feature, the Brute-force matching method [29,30] with the RANSAC algorithm. In particular, the PCB features are extracted and collated with the template data based on the ORB feature.…”

Section: Introductionmentioning

confidence: 99%

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A real-time defect detection in printed circuit boards applying deep learning

Nguyen

Bui

2022

Eureka: PE

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Inspection of defects in the printed circuit boards (PCBs) has both safety and economic significance in the 4.0 industrial manufacturing. Nevertheless, it is still a challenging problem to be studied in-depth due to the complexity of the PCB layouts and the shrinking down tendency of the electronic component size. In this paper, a real-time automated supervision algorithm is proposed to test the PCBs quality among different scenarios. The density of the PCBs layout and the complexity on the surface are analyzed based on deep learning and image feature extraction algorithms. To be more detailed, the ORB feature and the Brute-force matching method are utilized to match perfectly the input images with the PCB templates. After transferring images by aiding the RANSAC algorithm, a hybrid method using modern computer vision algorithms is developed to segment defective areas on the PCBs surface. Then, by applying the enhanced Residual Network –50, the proposed algorithm can classify the groove defects on the surface mount technology electronic components which minimum size up to 1x3 mm. After the training process, the proposed system is capable to categorize various types of overproduced, recycled, and cloned PCBs. The speed of the quality testing operation maintains at a high level with an average precision rate up to 96.29 % in case of good brightness conditions. Finally, the computational experiments demonstrate that the proposed system based on deep learning can obtain superior results and it outperforms several existing works in terms of speed, precision, and robustness

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Section: Fig 1 Flowchart Of the Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A real-time defect detection in printed circuit boards applying deep learning

Nguyen

Bui

2022

Eureka: PE

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“…In [5], Nguyen et al presented a paper real-time target human tracking using CAMSHIFT and LucasKanade optical flow algorithm. In their approach, the authors combined the CAMSHIFT algorithm and Lucas-Kanade Optical Flow Algorithm (LK-OFA) to create a more accurate and efficient tracking system.…”

Section: Introductionmentioning

confidence: 99%

Robust, Resilient Enhanced CAMSHIFT Model: Advancing Face Detection and Tracking Stability in Challenging Environments

Alex Kok Bin See,

Keith Han Wee Goh

2024

Malaysian J. Sci. Adv. Tech.

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This paper presents the development of a robust CAMSHIFT model for theoretical face detection and tracking. The proposed model integrates innovative techniques such as Perceptual Grouping, three Connected Component Operators, Weighted Adaptive Colour Histogram, and Selective Adaptation. Experimental results highlight its superior performance across scenarios like occlusions, varying illumination, near/far face tracking, skin-like background tracking, and disturbance from multiple faces. The normalized log-likelihood index serves as a robust indicator for face tracking analysis. Connected Component operations provide strong markers for error detection in video sequences. The enhanced CAMSHIFT algorithm exhibits resilience and stability, even in the presence of occlusions. Comparisons with the original CAMSHIFT reveal the enhanced model's superiority, extending tracking range to 500 cm, a calculated enhancement of 42.9 percent improvement. The study consistently favours the robust and resilient CAMSHIFT model in tracking against skin-like backgrounds and disturbances. Despite webcam convenience in used for algorithm development, the benefits of high-performance camera systems are envisioned for future research. This model is a significant advancement in face detection methods, promising improved adaptability and tracking capabilities.

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“…To improve the disadvantage of similar color or complex context, a lot of methods are proposed to improve the accuracy of target tracking such as the point-based Kanade Lukas Tomasi colour-flter (PKLTF) [11] is a single-target tracker which is constructed based on the Kanade Lukas Tomasi approach and Meanshift algorithm. It is a robust method that can deal with high appearance changes in the target, occlusion, and loss of the target while the tracking process.…”

Section: Introductionmentioning

confidence: 99%

An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter

Nguyen

Chu

Phan

et al. 2023

Journal of Robotics

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Automated human tracking in real time has been applied in many areas such as security, surveillance, traffic control, and robots. In this paper, an improvement of the Camshift human tracking algorithm based on deep learning and the Kalman filter is proposed. To detail an approach by using YOLOv4-tiny to detect a human in real time, Camshift is used to track a particular person and the Kalman filter is applied to enhance the performance of this algorithm in case of occlusion, noise, and different light conditions. The experiments show that the combination of YOLOv4-tiny and the improved Camshift algorithm raises the standard of speed as well as robustness. The proposed algorithm is suitable for running in real time and adapts well to the same color and different light conditions.

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Real-time Target Human Tracking using Camshift and LucasKanade Optical Flow Algorithm

Cited by 5 publications

References 36 publications

A real-time defect detection in printed circuit boards applying deep learning

A real-time defect detection in printed circuit boards applying deep learning

Robust, Resilient Enhanced CAMSHIFT Model: Advancing Face Detection and Tracking Stability in Challenging Environments

An Improvement of the Camshift Human Tracking Algorithm Based on Deep Learning and the Kalman Filter

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