中文摘要：

针对复杂场景下目标跟踪中目标出现的外观特征变化和遮挡问题，提出一种结合超像素和广义霍夫变换的在线实时目标跟踪算法．该算法从上下文中提取局部特征作为支持因子，构建一个混合的判别产生式对象模型．利用该模型，通过霍夫投票预测目标的中心位置，再通过判别式投票对目标和背景进行概率估计．对图像进行超像素分割，将之前的投票结果映射到对应的超像素，生成基于超像素的概率分布图像．采用贝叶斯跟踪框架，根据后验概率最大化，在概率分布图像基础上确定目标的位置．实验表明，该算法在复杂环境下目标跟踪的过程中对目标发生的形变和遮挡现象有很强的鲁棒性，能够实现准确稳定的在线目标跟踪．

英文摘要：

It is a great challenge to track an object robustly when variations occur such as changes in illumination, appearance or partial occlusion. In this paper, we propose a target tracking algorithm combining superpixel and hybrid Hough voting. Local features are extracted from the context as supporters to construct a hyhrid voting model. By this model, the target center is estimated by the Hough voting scheme. Local features are also distinguished to vote for the target and background, respectively. These voting results are combined into superpixels. Finally, the tracking task is formulated as the maximum a posterior estimate in the voting space. We demonstrate the performance of the algorithm on several public video sequences, which shows that our method is better than other online tracking approaches.