EPFL IC ISIM CVLAB, BC 309 (Batiment BC), Station 14, Lausanne, Switzerland.
IEEE Trans Pattern Anal Mach Intell. 2013 May;35(5):1263-73. doi: 10.1109/TPAMI.2012.209.
Methods for tracking an object have generally fallen into two groups: tracking by detection and tracking through local optimization. The advantage of detection-based tracking is its ability to deal with target appearance and disappearance, but it does not naturally take advantage of target motion continuity during detection. The advantage of local optimization is efficiency and accuracy, but it requires additional algorithms to initialize tracking when the target is lost. To bridge these two approaches, we propose a framework for unified detection and tracking as a time-series Bayesian estimation problem. The basis of our approach is to treat both detection and tracking as a sequential entropy minimization problem, where the goal is to determine the parameters describing a target in each frame. To do this we integrate the Active Testing (AT) paradigm with Bayesian filtering, and this results in a framework capable of both detecting and tracking robustly in situations where the target object enters and leaves the field of view regularly. We demonstrate our approach on a retinal tool tracking problem and show through extensive experiments that our method provides an efficient and robust tracking solution.
基于检测的跟踪和通过局部优化的跟踪。基于检测的跟踪的优点是它能够处理目标的出现和消失，但它在检测过程中不能自然地利用目标运动的连续性。局部优化的优点是效率和准确性，但当目标丢失时，它需要额外的算法来初始化跟踪。为了弥合这两种方法之间的差距，我们提出了一个将统一检测和跟踪作为时间序列贝叶斯估计问题的框架。我们方法的基础是将检测和跟踪都视为一个序列熵最小化问题，其目标是确定每帧中描述目标的参数。为此，我们将主动测试（AT）范式与贝叶斯滤波相结合，这使得该框架能够在目标对象定期进入和离开视场的情况下进行稳健的检测和跟踪。我们在视网膜工具跟踪问题上展示了我们的方法，并通过广泛的实验表明，我们的方法提供了一种高效和鲁棒的跟踪解决方案。
