Continuous Energy Minimization for Multitarget Tracking
Continuous Energy Minimization for Multitarget Tracking
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DOI：
10.1109/tpami.2013.103
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发表时间：
2014-01-01
影响因子：
23.6
通讯作者：
Schindler, Konrad
中科院分区：
文献类型：
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作者：
Milan, Anton;Roth, Stefan;Schindler, Konrad
多目标跟踪的许多最新进展旨在在时间窗口内找到一组（接近）最优的轨迹。为了处理可能的轨迹假设的大空间，通常通过某种形式的数据驱动或规则离散化将其减少到有限集合。在这项工作中，我们提出了一种替代配方的多目标跟踪的连续能量最小化。与最近的方法相反，我们专注于设计一个能量，对应于一个更完整的表示的问题，而不是一个服从全局优化。除了图像证据之外，能量函数还考虑了物理约束，例如目标动态、互斥和跟踪持续性。此外，部分图像证据处理与显式遮挡推理，和不同的目标是消歧与外观模型。然而，要找到强的局部极小的建议的非凸能量，我们构建了一个合适的优化方案，交替连续共轭梯度下降和离散transdimensional跳跃移动。执行这些移动，使得它们总是减少能量，允许搜索逃避弱极小值，并探索不同维度的搜索空间的更大部分。我们证明了我们的方法的有效性与广泛的定量评价几个公共数据集。
Many recent advances in multiple target tracking aim at finding a (nearly) optimal set of trajectories within a temporal window. To handle the large space of possible trajectory hypotheses, it is typically reduced to a finite set by some form of data-driven or regular discretization. In this work, we propose an alternative formulation of multitarget tracking as minimization of a continuous energy. Contrary to recent approaches, we focus on designing an energy that corresponds to a more complete representation of the problem, rather than one that is amenable to global optimization. Besides the image evidence, the energy function takes into account physical constraints, such as target dynamics, mutual exclusion, and track persistence. In addition, partial image evidence is handled with explicit occlusion reasoning, and different targets are disambiguated with an appearance model. To nevertheless find strong local minima of the proposed nonconvex energy, we construct a suitable optimization scheme that alternates between continuous conjugate gradient descent and discrete transdimensional jump moves. These moves, which are executed such that they always reduce the energy, allow the search to escape weak minima and explore a much larger portion of the search space of varying dimensionality. We demonstrate the validity of our approach with an extensive quantitative evaluation on several public data sets.