论文标题
在对象跟踪中应用R-pationgram以进行遮挡处理
Applying r-spatiogram in object tracking for occlusion handling
论文作者
论文摘要
对象跟踪是计算机视觉中最重要的问题之一。视频跟踪的目的是提取目标或目标对象的轨迹，即，在视频序列中准确地定位了移动目标，并将目标与序列特征空间中的非目标区分开。因此，特征描述符可能会对这种歧视产生重大影响。在本文中，我们使用许多跟踪器的基本思想，这些想法包括参考模型的三个主要组成部分，即对象建模，对象检测和本地化以及模型更新。但是，我们的系统有重大改进。我们的FORTH组件（遮挡处理）利用R-pationgram来检测最佳目标候选者。尽管空间图在像素的坐标上包含一些矩，但R-Spatiogram在图像中给定特征的分布计算基于区域的紧凑性，这些分布捕获了代表对象的更丰富的特征。拟议的研究开发了一种有效而健壮的方法，可以在存在明显的外观变化和严重遮挡的情况下继续跟踪整个视频序列。在普林斯顿RGBD跟踪数据集上评估了所提出的方法，该数据集考虑了具有不同挑战的序列，获得的结果证明了该方法的有效性。
Object tracking is one of the most important problems in computer vision. The aim of video tracking is to extract the trajectories of a target or object of interest, i.e. accurately locate a moving target in a video sequence and discriminate target from non-targets in the feature space of the sequence. So, feature descriptors can have significant effects on such discrimination. In this paper, we use the basic idea of many trackers which consists of three main components of the reference model, i.e., object modeling, object detection and localization, and model updating. However, there are major improvements in our system. Our forth component, occlusion handling, utilizes the r-spatiogram to detect the best target candidate. While spatiogram contains some moments upon the coordinates of the pixels, r-spatiogram computes region-based compactness on the distribution of the given feature in the image that captures richer features to represent the objects. The proposed research develops an efficient and robust way to keep tracking the object throughout video sequences in the presence of significant appearance variations and severe occlusions. The proposed method is evaluated on the Princeton RGBD tracking dataset considering sequences with different challenges and the obtained results demonstrate the effectiveness of the proposed method.
