توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Object Tracking Using Improved CAMShiftAlgorithm Combined with Motion Segmentation
نویسنده(ها): ،
اطلاعات انتشار: هفتمین کنفرانس ماشین بینایی و پردازش تصویر، سال
تعداد صفحات: ۴
Continuously adaptive mean–shift(CAMShift) is anefficient and light–weight tracking algorithm developed based onmean–shift. While color based CAMShift is suitable for trackingtargets in simple cases, it fails to track objects in more complexsituations. In this paper we review our low cost extension toimprove the traditional CAMShift algorithm. Combining theoriginal algorithm with a motion segmentation phase, weproposed an improved CAMShift algorithm to cope withCAMShift`s tracking problems. We evaluated the efficiency ofour approach by comparing our tracking results with thetraditional algorithm`s results in several cases<\div>

۲Online Failure Detection and Correction for CAMShift Tracking Algorithm
نویسنده(ها): ، ،
اطلاعات انتشار: هشتمین کنفرانس ماشین بینایی و پردازش تصویر، سال
تعداد صفحات: ۴
Tracking failure is an inevitable problem in any objecttracking algorithm. Online evaluation of a tracking algorithm todetect and correct failures is therefore an important task in anyobject tracking system. In this paper we propose an earlytracking failure detection procedure for the ContinuouslyAdaptive Mean–Shift(CAMShift) tracking algorithm. We alsopropose an algorithm to modify the tracker in order to correctthe detected failures. CAMShift is a light–weight trackingalgorithm first developed based on mean–shift to track humanface as a component in a perceptual user interface, but it easilyfails in tracking targets in more complex situations likesurveillance applications. With our proposed failure detectionand correction algorithm, CAMShift shows promising results inthe test video sequences<\div>

۳A Visual Tracking Algorithm Based on CAMShift and Motion Segmentation
نویسنده(ها):
اطلاعات انتشار: International Journal Information and Communication Technology Research، چهارم،شماره۳، Jun ۲۰۱۲، سال
تعداد صفحات: ۸
Continuously adaptive mean–shift (CAMShift) is an efficient and light–weight tracking algorithm firstly developed based on mean–shift to track human face in a perceptual user interface. CAMShift tracks a target by searching for the most similar areas to the target region in frames of a video sequence in regard to its reference color histogram. While color based CAMShift is suitable for tracking targets in simple cases, it fails to track objects in more complex situations. In this paper we review our low cost extension to improve the traditional CAMShift algorithm. Combining the original algorithm with simple motion segmentation techniques, we proposed an improved CAMShift algorithm to cope with CAMShift`s tracking problems. We have evaluated the efficiency of our approach through analyzing our tracking results. We have compared our results with other tracking algorithms in various tracking scenarios.
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