一种新的基于在线采样的多物体跟踪方法

Multiple Objects Tracking Based on Online Sampling

跟踪多个运动物体,尤其是在遮挡过程中跟踪多个运动物体,是计算机视觉领域一个重要但具有挑战性的问题.该文提出了一种新的在线采样、更新学习和分类的跟踪框架来处理多物体跟踪问题.首先,对遮挡发生前若干帧的各物体进行块采样,作为训练样本进行在线分类器设计.各帧的物体区域也在线进行块采样,并用这些分类器来进行分类标号.如果遮挡没有发生,一些新的训练样本被添加用来更新分类器.当遮挡发生时,根据标号结果,前景区域被分割成多个目标物体.和以往方法相比,新方法不依赖于一些假设条件,如场景深度信息、物体的先验模型(比如形状、种类、区域内颜色各向同性、运动规律等),具有更好的适应能力.实验结果验证了该文方法的稳定性和有效性.

Multiple objects tracking, especially multiple objects tracking with occlusion, is an important and challenging problem in computer vision. This paper proposes a novel on-line sample based framework to deal with this problem. First, training samples are obtained from each isolated object before occlusion occurs. A simple sample based scheme is used to produce a set of classifiers. The objects in the following frames are labeled using the classifiers. If no occlusion occurs in the frame, the classifiers will be updated by adding new training samples. When there is occlusion in the present frame, according to the labeled results, the foreground region can be segmented into multiple objects. Compared with previous works, this approach needs neither the depth information of the scene nor the prior models of objects such as color blobs, the type of objects （person or vehicle）, velocity assumption. Experiments show that the proposed approach can work robustly under the more complex conditions in which the above assumptions may be unreliable.