Visual object-tracking is a fundamental task applied in many applications of computer vision. Particle filter is one of the techniques which has been widely used in object tracking. Due to the virtue of extendability and flexibility on both linear and non-linear environments, various particle filter-based trackers have been proposed in the literature. However, the conventional approach cannot handle very large videos efficiently in the current data intensive information age. In this work, a parallelized particle filter is provided in a distributed framework provided by the Hadoop/Map-Reduce infrastructure to tackle object-tracking tasks. The experiments indicate that the proposed algorithm has a better convergence and accuracy as compared to the traditional particle filter. The computational power and the scalability of the proposed particle filter in single object tracking have been enhanced as well.
计算机技术和通信技术的共同发展,使得数据呈现指数大爆炸式的增长。数据中蕴含的巨大价值是有目共睹的。但是对数据集的肆意收集与分析,使用户的隐私数据处在被泄露的风险中。为保护用户的敏感数据的同时实现对基数查询的有效响应,提出一种基于差分隐私的隐私保护算法BFRRCE(Bloom Filter Random Response for Cardinality Estimation)。首先对用户的数据利用Bloom Filter数据结构进行数据预处理,然后利用本地差分隐私的扰动算法对数据进行扰动,达到保护用户敏感数据的目的。
