基于轨迹提取算法的视频关联动作跟踪仿真

针对已有视频关联跟踪方法无法准确提取关联动作轨迹, 导致视频关联动作跟踪结果出现较大偏差, 且跟踪速率较低的问题, 提出一种基于轨迹提取算法的视频关联动作跟踪方法. 首先, 根据多元组理念组建多元组轨迹提取模型, 划分运动视频图像特征分布矢量化集合, 计算视频图像分割支持向量机临界值; 其次, 通过颜色系统分离像素特征, 利用虚拟视景重构输出关联动作轨迹提取值; 再次, 在多粒度滤波器训练中设置预期输出值, 采用Fourier变换将卷积计算转变成点乘运算, 计算各粒度下边界最小矩形重叠率; 最后, 通过欧氏距离获得两个边界最小矩阵变换情况, 明确各粒度的轨迹波动程度, 完成视频关联动作跟踪全过程. 实验结果表明, 该方法的视频关联动作跟踪速率为14.9 帧/s, 能有效提高目标跟踪速率, 实现精准的视频关联动作跟踪.

Simulation of Video Association Motion Tracking Based on Trajectory Extraction Algorithm

Aiming at the problems that the existing video association tracking methods could  not accurately extract the association motion trajectory, which led to large deviation in the video association motion  tracking results and low tracking rate, the author proposed a video association motion tracking method based on trajectory extraction algorithm. Firstly, according to the idea of multivariate group, a multivariate group trajectory extraction model was established, the feature distribution vectorization set of moving video image was divided, and the critical value of video image segmentation support vector machine was calculated. Secondly, the pixel features were separated by the color system, and the extracted values of association motion trajectory were output by virtual scene reconstruction. Thirdly, the expected output value was set in the multi granularity filter training, and the Fourier transform was used to transform convolution calculation into point multiplication operation to calculate the minimum rectangular overlap rate of the boundary under each granularity. Finally, the minimum matrix transformation of two boundaries was obtained by Euclidean distance, the trajectory fluctuation degree of each granularity was defined, and the whole process of video association motion tracking was completed. The experimental results show that the video association motion tracking rate of the proposed method is 14.9 frame/s, which can effectively improve the target tracking rate and achieve accurate video association motion tracking.