欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
提出了一种融合多模型的粒子滤波跟踪新算法（MMGPF），并将其应用于行人与汽车跟踪。此跟踪算法特点在于：（1）将Camshift跟踪算法和AdaBoost分类器的输出作为观测值优化建议概率分布；同时，改进粒子滤波的算法结构，有效地提高了粒子滤波的采样效率；在不影响跟踪性能的情况下，大幅减少了跟踪所需粒子数。（2）用两种描绘子提高对似然性的估计：（3）采用两种有效措施提高算法的实时性。通过多模型融合，有效地解决了目标跟踪过程中由于目标相互遮挡、目标消失再重现、光照变化和目标与背景颜色相近所造成的跟踪丢失。行人和汽车的跟踪试验结果证明该算法具有鲁棒性和实时性。
英文摘要：
A novel algorithm for integrating multiple model into particle filter, MMGPF, was proposed for moving target tracking and applied to pedestrian and vehicle tracking. It has some innovative characters： （a） The proposal probability distribution was optimized by incorporating the outputs of Camshifl and AdatBoost into the IDPF framework, and the framework of particle filtering was improved, leading to efficiency improvement of the particle filter sampling and dramatically reduction of particle numbers without affecting the tracking performance. （b） Using two descriptors, the HOG descriptor and HSV color histogram, to enhance observation model. （c） Using two kinds of method to speed up the proposed algorithm. Owing to integrating multiple models, the MMGPF implicitly handles the difficulties of tracking caused by object occlusion, object disappearance and reappearance, illumination variation and background clutters. It is demonstrated through several real tracking tasks that the new method performs well in both tracking robustness and computational efficiency.
同期刊论文项目
同项目期刊论文
位置：