基于CUDA 与粒子滤波的多特征融合视频目标跟踪算法

针对复杂环境中非线性运动目标跟踪，单一特征无法满足对目标的准确描述，造成不能准确跟踪的问题，提出了一种基于粒子滤波与多种特征自适应融合的跟踪方法．该方法先对目标区域提取轮廓方向分布与颜色分布。根据自适应规则融合后，然后与粒子滤波理论相结合，实现对各种复杂环境中视频运动目标的有效跟踪．同时，通过使用CUDA（ComputeUnifiedDevicemchitecture）加速，实现了目标跟踪的实时性．实验结果表明，该方法可对非线性、非高斯的运动目标进行有效的跟踪，对目标的遮挡与暂时消失，背景焦距的拉伸与背景颜色的变换，有很强的鲁棒性和实时性．

Multi-Feature Fusion Video Object Tracking Algorithm Based on CUDA and Particle Filter

To solve the problem that single feature can't describe the nonlinear moving targets accurately under complex environment, this paper propopsed a adaptive method which is based on multiple features and particle filter.It first extracted contour direction distribution and color distribution from the target area.And then with help of particle filter theory, it integrated these features to track nonlinear moving targets from the video. In the end, CUDA can provide powerful parallel computing capacity so that this proposed method can be accelerated and meet the need of real-time monitoring. The experiment results of this paper indicates that the proposed method has strong robustness and high accurateness for non-linear and non-Gaussian moving targets. Moreover, it can cope with the situations of targets occlusion or temporarily disappering, focal length transformation and background changing.