Yilmaz Alper, Li Xin, Shah Mubarak
School of Computer Science, University of Central Florida, 4000 Central Florida Blvd., Orlando, FL 32816, USA.
IEEE Trans Pattern Anal Mach Intell. 2004 Nov;26(11):1531-6. doi: 10.1109/TPAMI.2004.96.
We propose a tracking method which tracks the complete object regions, adapts to changing visual features, and handles occlusions. Tracking is achieved by evolving the contour from frame to frame by minimizing some energy functional evaluated in the contour vicinity defined by a band. Our approach has two major components related to the visual features and the object shape. Visual features (color, texture) are modeled by semiparametric models and are fused using independent opinion polling. Shape priors consist of shape level sets and are used to recover the missing object regions during occlusion. We demonstrate the performance of our method on real sequences with and without object occlusions.
我们提出了一种跟踪方法,该方法能够跟踪完整的物体区域,适应不断变化的视觉特征并处理遮挡情况。跟踪是通过在由一个带定义的轮廓附近评估的某个能量泛函最小化,使轮廓逐帧演化来实现的。我们的方法有两个与视觉特征和物体形状相关的主要组成部分。视觉特征(颜色、纹理)由半参数模型建模,并使用独立意见投票进行融合。形状先验由形状水平集组成,用于在遮挡期间恢复缺失的物体区域。我们在有和没有物体遮挡的真实序列上展示了我们方法的性能。