基于字典学习的实时运动目标跟踪算法

采用提取图像的尺度不变特征可以获得较好的匹配跟踪效果，但该特征提取方法比较耗时。针对这一问题，提出了一种鲁棒的实时目标跟踪方法。该方法通过提取目标的多尺度平移、旋转特征来构建字典，提高了算法的鲁棒性。利用所构建的字典来表示待跟踪目标集特征，查找与目标模板最近邻的待跟踪目标，即可确定跟踪的最终结果。试验结果表明，这种基于字典学习的实时跟踪算法可以鲁棒实时地跟踪单目标。

Robust Real-time Object Tracking Based on Dictionary Learning

Through the matching of scale-invariant visual features, satisfactory tracking results can usual- ly be obtained. However, this approach computationally is very expensive. To overcome the challenging problem, a real-time object tracking method is presented based on feature matching. In the proposed ap- proach, multi-scale translation and rotation invariant features are extracted to build a dictionary with good robustness. And then, the target is represented by coding under this dictionary. Finally, the tracking re- sult is obtained by exhaustively searching for the one that best matches the target model among all the candidates. Experiment results show that this method can achieve real-time and robust tracking target.