Tracking video objects with feature points based particle filtering
Tracking video objects with feature points based particle filtering
复制标题
使用基于粒子过滤的特征点跟踪视频对象
DOI：
10.1007/s11042-010-0676-y
复制
发表时间：
2012-05-01
影响因子：
3.6
通讯作者：
Zhang, Jun
中科院分区：
文献类型：
--
作者：
Gao, Tao;Li, Guo;Zhang, Jun
对于智能视频监控来说，多个运动目标的自适应跟踪仍然是一个悬而未决的问题。本文提出了一种新的基于视频帧的多目标跟踪方法。提出了一种与 SIFT（尺度不变特征变换）相结合的粒子滤波用于运动跟踪，其中 SIFT 关键点被视为粒子的一部分以改善样本分布。然后，采用队列链方法记录不同对象之间的数据关联，可以提高检测精度并降低计算复杂度。通过实际道路测试和比较，该系统在多目标跟踪方面具有较好的性能，例如实时实现和抗相互遮挡的鲁棒性，表明该系统对于智能视频监控系统是有效的。
For intelligent video surveillance, the adaptive tracking of multiple moving objects is still an open issue. In this paper, a new multi-object tracking method based on video frames is proposed. A type of particle filtering combined with the SIFT (Scale Invariant Feature Transform) is proposed for motion tracking, where SIFT key points are treated as parts of particles to improve the sample distribution. Then, a queue chain method is adopted to record data associations among different objects, which could improve the detection accuracy and reduce the computational complexity. By actual road tests and comparisons, the system tracks multi-objects with better performance, e.g., real time implementation and robust against mutual occlusions, indicating that it is effective for intelligent video surveillance systems.