基于高斯变异粒子群算法的人体活动发现
A Novel Skeleton-Based Human Activity Discovery Using Particle Swarm Optimization with Gaussian Mutation
摘要：
人类活动发现的目的是在没有任何关于定义每个活动的先验信息的情况下对人类执行的活动进行聚类。人类活动识别中提出的大多数方法都是有监督的，其中有标记的输入来训练系统。在现实生活中，由于活动数据量巨大，人类活动的多样性，很难对活动数据进行标注。本文提出了一个无监督的框架，执行人体活动发现的三维骨架序列。首先，提出了一种数据预处理方法。在这个阶段中，基于动能选择重要帧。接着，提取关节的位移、统计位移、角度和方向特征来表示活动信息。由于并非所有提取的特征都具有有用的信息，因此使用PCA降低特征的维数。大多数人类活动发现方法都不是完全无监督的。他们在对活动进行分类之前使用预先分割的视频。为了解决这个问题，我们使用了一个滑动时间窗口来分割活动的时间序列与一些重叠。然后，活动发现我们提出的混合粒子群优化（PSO）与高斯变异和K-均值（HPGMK）算法，以提供不同的解决方案。粒子群算法由于其简单的思想和强大的全局搜索能力，可以在几次迭代中找到理想的解决方案。最后，k-means被应用到粒子群算法每次迭代的结果质心，以克服粒子群算法收敛速度慢的问题。在五个数据集上的实验结果表明，与其他最先进的方法相比，所提出的框架在发现活动方面具有优越的性能，并且平均提高了至少4%的准确性。
英文摘要：
Human activity discovery aims to cluster the activities performed by humans without any prior information on what defines each activity. Most methods presented in human activity recognition are supervised, where there are labeled inputs to train the system. In reality, it is difficult to label activities data because of its huge volume and the variety of human activities. This paper proposes an unsupervised framework to perform human activity discovery in 3D skeleton sequences. First, an approach for data pre-processing is presented. In this stage, important frames are selected based on kinetic energy. Next, the displacement of joints, statistical displacements, angles, and orientation features are extracted to represent the activities information. Since not all extracted features have useful information, the dimension of features is reduced using PCA. Most methods proposed for human activity discovery are not fully unsupervised. They use pre-segmented videos before categorizing activities. To deal with this, we have used a sliding time window to segment the time series of activities with some overlapping. Then, activities are discovered by our proposed Hybrid Particle swarm optimization (PSO) with Gaussian Mutation and K-means (HPGMK) algorithm to provide diverse solutions. PSO is used due to its straightforward idea and powerful global search capability which can identify the ideal solution in a few iterations. Finally, k-means is applied to the outcome centroids from each iteration of the PSO to overcome the slow convergence rate of PSO. The experiment results on five datasets show that the proposed framework has superior performance in discovering activities compared to the other state-of-the-art methods and has increased accuracy of at least 4% on average.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
