一种鲁棒的自适应带宽跟踪方法

在自适应带宽均值移动算法的基础上, 引入粒子滤波, 提出一种新的目标跟踪方法. 该方法通过更新带宽矩阵以适应目标尺度的变化; 采用加权和方法融合定位检测结果, 使跟踪不易陷入局部最优状态; 对粒子进行收敛采样, 维持粒子多样性, 减小累积误差; 提出一种目标扩展搜索策略, 用于目标丢失后重新搜索跟踪目标. 实验结果表明, 所提出的跟踪方法在复杂场景中表现出了较好的鲁棒性, 且跟踪轨迹平滑.

A robust bandwidth-adaptive object tracking method

Particle filter is introduced into the adaptive mean shift tracking algorithm, and a new object tracking method is proposed, which tracks object scale with a dynamic band-matrix. It combines the detection results to determine the object location with a weighted summation method, and avoids the system from falling into local optimum. All the particles are converged and re-sampled in a place near the precisely determined location, keeping the diversity with fewer particles and reducing the accumulated error. An extended searching strategy is proposed to be used in target re-search once it is lost. Experiment results show that the proposed method is robust in complex environment and the tracking trajectory is smooth.