Remote Sensing Image Target Recognition System of Tennis Sports based on CAMSHIFT Algorithm

Jiang, Zerong; Li, Ruoguo; Chen, Zhu

doi:10.1109/iciscet56785.2022.00042

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…As in [2], Jiang et al reported and discussed about a remote sensing image target recognition system specifically designed for tennis sports in China. It utilizes the CAMSHIFT (Continuously Adaptive Mean Shift) algorithm as a central component for moving target detection and tracking.…”

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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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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Enhanced CAMSHIFT with Perceptual Grouping, Weighted Histogram, Selective Adaptation and Kalman Filtering: Improving Stability and Accuracy in Face Detection and Tracking

Alex Kok Bin See,

Ji Xing

2024

Malaysian J. Sci. Adv. Tech.

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This paper reports an enhanced CAMSHIFT model for theoretical face detection and tracking. Detection is performed based on a statistical characterisation of skin-colour. The model integrates advanced techniques including Perceptual Grouping, Weighted histogram distribution, Selective Adaptation and with Kalman Filtering to increase the face detection and tracking strategies. Results reveal increased performance in scenarios like hand occlusions, varying illumination, disturbance from multiple faces. The normalized log-likelihood index serves as consistent indicator for face tracking analysis. This new model with Kalman filtering can predict the face’s centroid position and increased tracking stability. This new model has achieved low Mean Absolute Percentage Error (MAPE) both predicted (X^) and (Y^) at 9.32 % and 9.70 % respectively. Low RMSE values of X and Y coordinates reported as 8.3 pixel and 8.8 pixel suggest that the Kalman Filtering predicted values are reliable and accurate. It strongly indicates that on average, the forecast/predicted by Kalman Filtering algorithm deviates from the actual values by low margin and this model is effective in predicting and tracking the face target. Further observation suggests that the x-errors and y-errors have both positive and negative values, suggesting no systematic bias in over- or under-prediction in this developed Kalman Filtering model. This model is a significant advancement in face detection methods, promising improved adaptability and tracking.

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Remote Sensing Image Target Recognition System of Tennis Sports based on CAMSHIFT Algorithm

Cited by 2 publications

References 9 publications

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

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

Enhanced CAMSHIFT with Perceptual Grouping, Weighted Histogram, Selective Adaptation and Kalman Filtering: Improving Stability and Accuracy in Face Detection and Tracking

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