基于 Kinect 的矿井人员违规行为识别算法研究

由于煤炭生产的特殊性与危险性,煤炭生产过程中安全事故常有发生,其中人为因素占极高的比例,因此研究矿井工作人员的违规行为十分必要.针对人体行为识别中传统的动态时间规整算法经常出现的奇异点和时间复杂度问题,提出一种分段线性逼近结合自适应权重动态时间规整算法.对该算法进行了仿真以及实验,该算法在SDU Fall Dataset数据集的平均识别率达到了95.33%,平均识别时间减少了46.47%,在煤矿井下使用该系统进行测试,结果表明所提出的算法在识别速度和准确率上均有一定程度的提高.

Research on Identification Algorithm of Mine Person's Violation Behavior Based on Kinect

Due to the particularity and danger of coal production, safety accidents often occur in the coal production process. Human factors account for a very high proportion. Therefore, it is necessary to study the violations of mine workers. Aiming at the singular point and time complexity problems often found in traditional dynamic time warping algorithms in human behavior recognition, a piecewise linear approximation algorithm combined with adaptive weight dynamic time warping algorithm is proposed. Then, the algorithm is simulated and experimented. The average recognition rate of the algorithm in the SDU Fall Dataset data set is 95.33%, and the average recognition time is reduced by 46.47%. Finally, we use the system to test in the coal mine. The results show that the proposed algorithm has a certain degree of improvement in recognition speed and accuracy.