Key Laboratory of Underwater Acoustic signal Processing of Ministry of Education, Southeast University, Nanjing, Jiangsu, China.
PLoS One. 2018 Aug 23;13(8):e0201872. doi: 10.1371/journal.pone.0201872. eCollection 2018.
In this paper, we propose a novel object tracking algorithm by using high-dimensional particle filter and combined features. Firstly, the refined two-dimensional principal component analysis and the tendency are combined to represent an object. Secondly, we present a framework using high-order Monte Carlo Markov Chain which considers more information and performs more discriminative and efficient on moving objects than the traditional first-order particle filtering. Finally, an advanced sequential importance resampling is applied to estimate the posterior density and obtains the high-quality particles. To further gain the better samples, K-means clustering is used to select more typical particles, which reduces the computational cost. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the performance of our proposed algorithm is superior to the state-of-the-art methods.
本文提出了一种新的目标跟踪算法，该算法使用高维粒子滤波器和组合特征。首先，将改进的二维主成分分析和趋势相结合来表示一个物体。其次，提出了一种使用高阶蒙特卡罗马尔可夫链的框架，该框架考虑了更多的信息，并且比传统的一阶粒子滤波更具判别力和效率。最后，应用先进的序贯重要性重采样来估计后验密度，并获得高质量的粒子。为了进一步获得更好的样本，使用 K-均值聚类来选择更典型的粒子，从而降低计算成本。在具有挑战性的图像序列上的定性和定量评估表明，我们提出的算法的性能优于最新方法。
