基于多特征相似性和轨迹滤波的鲁棒运动目标跟踪
Robust Mobile Object Tracking Based on Multiple Feature Similarity and Trajectory Filtering
摘要：
本文提出了一种新的算法来跟踪移动目标在不同的场景条件。提出的跟踪器的主要思想包括估计，多特征相似性度量和轨迹滤波。为每个跟踪对象定义特征集（距离，面积，形状比率，颜色直方图）以搜索最佳匹配对象。将其最佳匹配目标与卡尔曼滤波器估计的目标状态相结合，更新跟踪目标的位置和大小。然而，移动对象的轨迹通常是支离破碎的，因为遮挡和误检测。因此，我们也提出了一个轨迹过滤，称为全球跟踪器，旨在消除噪声轨迹和融合属于同一个移动对象的碎片轨迹。该方法已被测试与不同场景条件下的五个视频。其中三个是由ETISEO基准测试项目（http：//www-sop.inria.fr/orion/ETISEO）提供的，其中所提出的跟踪器性能与其他七种跟踪算法进行了比较。与现有技术相比，我们的方法的优点是：（i）不需要先验知识信息（例如，不需要校准和上下文模型），（ii）跟踪器通过组合多个特征相似性而更可靠，（iii）跟踪器可以在不同的场景条件下执行：单个/多个移动对象、弱/强照明、室内/室外场景，（iv）定义并应用轨迹滤波以提高跟踪器性能，（v）跟踪器性能优于现有技术的许多算法。
英文摘要：
This paper presents a new algorithm to track mobile objects in different scene conditions. The main idea of the proposed tracker includes estimation, multi-features similarity measures and trajectory filtering. A feature set (distance, area, shape ratio, color histogram) is defined for each tracked object to search for the best matching object. Its best matching object and its state estimated by the Kalman filter are combined to update position and size of the tracked object. However, the mobile object trajectories are usually fragmented because of occlusions and misdetections. Therefore, we also propose a trajectory filtering, named global tracker, aims at removing the noisy trajectories and fusing the fragmented trajectories belonging to a same mobile object. The method has been tested with five videos of different scene conditions. Three of them are provided by the ETISEO benchmarking project (http://www-sop.inria.fr/orion/ETISEO) in which the proposed tracker performance has been compared with other seven tracking algorithms. The advantages of our approach over the existing state of the art ones are: (i) no prior knowledge information is required (e.g. no calibration and no contextual models are needed), (ii) the tracker is more reliable by combining multiple feature similarities, (iii) the tracker can perform in different scene conditions: single/several mobile objects, weak/strong illumination, indoor/outdoor scenes, (iv) a trajectory filtering is defined and applied to improve the tracker performance, (v) the tracker performance outperforms many algorithms of the state of the art.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
