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RISS 인기검색어
- 검색키워드
- 저자 : ( Xing Hu Wenyan Ci ) (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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An Anti-occlusion and Scale Adaptive Kernel Correlation Filter for Visual Object Tracking
( Yingping Huang ),( Chao Ju ),( Xing Hu ),( Wenyan Ci ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.4
Focusing on the issue that the conventional Kernel Correlation Filter (KCF) algorithm has poor performance in handling scale change and obscured objects, this paper proposes an anti-occlusion and scale adaptive tracking algorithm in the basis of KCF. The average Peak-to Correlation Energy and the peak value of correlation filtering response are used as the confidence indexes to determine whether the target is obscured. In the case of non-occlusion, we modify the searching scheme of the KCF. Instead of searching for a target with a fixed sample size, we search for the target area with multiple scales and then resize it into the sample size to compare with the learnt model. The scale factor with the maximum filter response is the best target scaling and is updated as the optimal scale for the following tracking. Once occlusion is detected, the model updating and scale updating are stopped. Experiments have been conducted on the OTB benchmark video sequences for compassion with other state-of-the-art tracking methods. The results demonstrate the proposed method can effectively improve the tracking success rate and the accuracy in the cases of scale change and occlusion, and meanwhile ensure a real-time performance.
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Lane Detection Based on Inverse Perspective Transformation and Kalman Filter
( Yingping Huang ),( Yangwei Li ),( Xing Hu Wenyan Ci ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.2
This paper proposes a novel algorithm for lane detection based on inverse perspective transformation and Kalman filter. A simple inverse perspective transformation method is presented to remove perspective effects and generate a top-view image. This method does not need to obtain the internal and external parameters of the camera. The Gaussian kernel function is used to convolute the image to highlight the lane lines, and then an iterative threshold method is used to segment the image. A searching method is applied in the top-view image obtained from the inverse perspective transformation to determine the lane points and their positions. Combining with feature voting mechanism, the detected lane points are fitted as a straight line. Kalman filter is then applied to optimize and track the lane lines and improve the detection robustness. The experimental results show that the proposed method works well in various road conditions and meet the real-time requirements.
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