光学技术, 2018, 44 (4): 427, 网络出版: 2018-08-30
基于复杂性度量与多尺度运动编码的图像动作识别算法
Action recognition algorithm based on complexity measure and multi-scale motion coding
图像动作识别 复杂性度量 多尺度运动 运动编码 k-均值聚类 条件随机场分类 image action recognition complexity measure multi-scale motion motion coding k- mean conditional random fields
摘要
人体动作的识别与理解是人机交互、机器人应用的关键技术之一, 为了提高人体各种复杂动作的识别精度与鲁棒性, 研究了基于复杂性度量与多尺度运动编码的动作识别技术。通过不同长度的滑动窗口对视频序列获取子序列;通过时间序列复杂性来度量人体运动轨迹, 设计了一种多尺度的滑动窗口, 从而选择出有效子序列;基于有效子序列, 引入k-均值聚类分析算法, 对人体运动进行编码, 获取运动编码直方图;引入条件随机场对动作分类学习, 完成动作识别与理解。所提出的算法在人机交互、智能家居、视频监控等领域具有较好的参考价值。
Abstract
The recognition and understanding of human motion was one of the key technologies in human-computer interaction and robot applications, in order to improve the recognition accuracy and robustness of various complex motions of human body, a motion recognition scheme based on complexity measurement and multi-scale motion coding is studied. For a video sequence, the subsequence is obtained by sliding windows of different lengths. The trajectory of human body was measured by time series complexity, thus, meaningful subsequences were selected; A meaningful subsequence based on selection, the k- mean clustering algorithm was introduced for motion coding, generative motion coded histogram. Moreover, in order to solve the sensitivity problem of fixed length sliding windows, a multi scale sliding window generation motion coding was designed. The conditional random field was introduced to classify the actions, complete action recognition and understanding. Therefore, the proposed algorithm has a good reference value in human-machine interaction, smart home, video surveillance and other fields.
邬厚民, 程谆. 基于复杂性度量与多尺度运动编码的图像动作识别算法[J]. 光学技术, 2018, 44(4): 427. WU Houmin, CHENG Zhun. Action recognition algorithm based on complexity measure and multi-scale motion coding[J]. Optical Technique, 2018, 44(4): 427.