基于视频粒子流和FTLE场的人群运动分割算法

针对复杂视频监控场景中不同运动行为的人群分割，提出了将视频粒子流和有限时间李雅普诺夫指数(FTLE)场相结合的人群运动分割算法。首先利用视频粒子流来表示长周期的粒子运动估计，通过最小化包含粒子外观匹配一致性和粒子间形变的能量函数，来优化每个粒子的轨迹；接着求解粒子流图的空间梯度，并构造FTLE场；最后利用FTLE场中的拉格朗日相干结构把流图分割成运动特性不同的区域。实验结构表明，算法能从拥挤复杂的视频监控场景中有效地分割出不同运动特性的群体，且具有较好的鲁棒性。

Crowd motion segmentation algorithm based on video particle flow and FTLE field

To segment moving crowd with different dynamics in complex video surveillance scenes, this paper proposed a crowd motion segmentation algorithm which was based on video particle flow and Finite Time Lyapunov Exponent (FTLE) field. Firstly, video particle flow was used to represent the long-range particle motion estimation. To optimize these particles trajectories, an energy function containing point-based appearance matching and distortion between the particles was minimized. Then the spatial gradient of the particle flow map was solved and the FTLE field was constructed. Finally, the Lagrangian Coherent Structure (LCS) in the FTLE field was used to divide flow into regions of qualitatively different dynamics. The experimental results show that the proposed algorithm can effectively segment crowd flow with different dynamics in complex video surveillance scenes, and it has strong robustness.