To understand daily events accurately, adaptive pose estimation (APE) systems require a robust context-aware model and optimal feature selection methods. In this paper, we propose a novel gait event detection (GED) system that consists of saliency silhouette detection, a robust body parts model and a 2D stick-model followed by a hierarchical optimization algorithm. Furthermore, the most prominent context-aware features such as energy, 0–180° intensity and distinct moveable features are proposed by focusing on invariant and localized characteristics of human postures in different event classes. Finally, we apply Grey Wolf optimization and a genetic algorithm to discriminate complex postures and to provide appropriate labels to each event. In order to evaluate the performance of proposed GED, two public benchmark datasets, UCF101 and YouTube, are examined via the n-fold cross validation method. For the two benchmark datasets, our proposed method detects the human body key points with 82.4% and 83.2% accuracy respectively. Also, it extracts the context-aware features and finally recognizes the gait events with 82.6% and 85.0% accuracy, respectively. Compared with other well-known statistical and state-of-the-art methods, our proposed method outperforms other similarly tasked methods in terms of posture detection and recognition accuracy.

为了准确地了解日常事件，自适应姿态估计（APE）系统需要强大的上下文感知模型和最佳特征选择方法。在本文中，我们提出了一种新颖的步态事件检测（GED）系统，该系统由显着性轮廓检测，鲁棒的身体部位模型和2D棒模型组成，然后是分层优化算法。此外，通过关注不同事件类别中人体姿势的不变性和局部性，提出了最突出的情境感知功能，例如能量，0-180°强度和独特的可移动功能。最后，我们应用Gray Wolf优化算法和遗传算法来区分复杂的姿势并为每个事件提供适当的标签。为了评估拟议的GED的性能，使用了两个公开的基准数据集UCF101和YouTube，n倍交叉验证方法。对于这两个基准数据集，我们提出的方法分别以82.4％和83.2％的准确度检测人体关键点。同样，它提取上下文感知特征，并最终分别以82.6％和85.0％的准确度识别步态事件。与其他众所周知的统计方法和最新方法相比，我们提出的方法在姿势检测和识别准确性方面优于其他类似任务方法。