National Laboratory for Information Science and Technology, Department of Automation, Tsinghua University, Beijing 100084, China.
IEEE Trans Image Process. 2012 Jul;21(7):3296-305. doi: 10.1109/TIP.2012.2190085. Epub 2012 Apr 5.
Visual prior from generic real-world images can be learned and transferred for representing objects in a scene. Motivated by this, we propose an algorithm that transfers visual prior learned offline for online object tracking. From a collection of real-world images, we learn an overcomplete dictionary to represent visual prior. The prior knowledge of objects is generic, and the training image set does not necessarily contain any observation of the target object. During the tracking process, the learned visual prior is transferred to construct an object representation by sparse coding and multiscale max pooling. With this representation, a linear classifier is learned online to distinguish the target from the background and to account for the target and background appearance variations over time. Tracking is then carried out within a Bayesian inference framework, in which the learned classifier is used to construct the observation model and a particle filter is used to estimate the tracking result sequentially. Experiments on a variety of challenging sequences with comparisons to several state-of-the-art methods demonstrate that more robust object tracking can be achieved by transferring visual prior.
可以学习和转移来自通用真实世界图像的视觉先验，以表示场景中的对象。受此启发，我们提出了一种算法，用于在线对象跟踪中转移离线学习的视觉先验。从一组真实世界的图像中，我们学习了一个过完备字典来表示视觉先验。对象的先验知识是通用的，并且训练图像集不一定包含目标对象的任何观察结果。在跟踪过程中，学习到的视觉先验被转移到稀疏编码和多尺度最大池化中，以构建对象表示。通过这种表示，在线学习线性分类器来区分目标和背景，并考虑目标和背景随时间的外观变化。然后在贝叶斯推断框架内进行跟踪，其中学习的分类器用于构建观测模型，而粒子滤波器用于顺序估计跟踪结果。在具有与几种最先进方法进行比较的各种挑战性序列上的实验表明，通过转移视觉先验可以实现更鲁棒的对象跟踪。
