图 书 馆
基于模型的不完全轨迹聚类方法的运动分割Motion segmentation by a model-based clustering approach of incomplete trajectories |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_karavasilis_trajectories/ |
| 主讲教师: | Vasileios Karavasilis |
| 开课单位: | 约阿尼纳大学 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper, we present a framework for visual object tracking based on clustering trajectories of image key points extracted from a video. The main contribution of our method is that the trajectories are automatically extracted from the video sequence and they are provided directly to a model-based clustering approach. In most other methodologies, the latter constitutes a difficult part since the resulting feature trajectories have a short duration, as the key points disappear and reappear due to occlusion, illumination, viewpoint changes and noise. We present here a sparse, translation invariant regression mixture model for clustering trajectories of variable length. The overall scheme is converted into a Maximum A Posteriori approach, where the Expectation-Maximization (EM) algorithm is used for estimating the model parameters. The proposed method detects the different objects in the input image sequence by assigning each trajectory to a cluster, and simultaneously provides the motion of all objects. Numerical results demonstrate the ability of the proposed method to offer more accurate and robust solution in comparison with the mean shift tracker, especially in cases of occlusions. |
| 关 键 词: | 聚类轨迹; 视觉对象; 期望最大化 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-03：lxf |
| 阅读次数: | 69 |