CAJ | 학술논문

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(Compute Unified Device Architecture)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。
침대복잡배경중비선성운동목표근종，단일특정무법만족대목표적준학묘술，조성불능준학근종적문제，제출료일충기우입자려파여다충특정자괄응융합적근종방법。해방법선대목표구역제취륜곽방향분포여안색분포，근거자괄응규칙융합후，연후여입자려파이론상결합，실현대각충복잡배경중시빈운동목표적유효근종。동시，통과사용CUDA(Compute Unified Device Architecture)가속，실현료목표근종적실시성。실험결과표명，해방법가대비선성、비고사적운동목표진행유효적근종，대목표적차당여잠시소실，배경초거적랍신여배경안색적변환，유흔강적로봉성화실시성。
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.