CAJ | 학술논문

在研究经典算法的基础上，提出了一种多技术融合的 Mean-Shift 目标跟踪算法，有效地解决了经典 Mean-Shift 跟踪算法存在的缺陷。通过 Kalman 算法预测估计目标的中心位置，通过分块颜色直方图提取目标区域的空间信息进行，同时采用背景加权和核加权相结合的方式抑制背景像素对目标的干扰。在多个视频数据上的试验结果表明，研究方法有效地克服了经典的 Mean-Shift 目标跟踪算法对遮挡、背景像素敏感的问题，在复杂环境的背景下对运动目标跟踪更加准确。
재연구경전산법적기출상，제출료일충다기술융합적 Mean-Shift 목표근종산법，유효지해결료경전 Mean-Shift 근종산법존재적결함。통과 Kalman 산법예측고계목표적중심위치，통과분괴안색직방도제취목표구역적공간신식진행，동시채용배경가권화핵가권상결합적방식억제배경상소대목표적간우。재다개시빈수거상적시험결과표명，연구방법유효지극복료경전적 Mean-Shift 목표근종산법대차당、배경상소민감적문제，재복잡배경적배경하대운동목표근종경가준학。
Based on the study of classic algorithm,a Mean-Shift target tracking algorithm fused multi-technology was proposed,and the defects of the classic Mean-Shift tracking algorithm were solved.The center position of target was es-timated by the Kalman algorithm.The space information of the target area was extracted using the block color histo-gram.The combination approach of the background weighted and nuclear weighted was used to suppress the interference of background pixels on the target.The experiments resulted on several video data showed that the new method fused three kinds of technology effectively overcame the barrier and background pixel sensitive problem,and had more accu-rate tracking than classic Mean-Shift target tracking algorithm under complex environment.