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Continuous Energy Minimization for Multitarget Tracking

机译:连续能量最小化,用于多目标跟踪

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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.
机译:多目标跟踪的许多最新进展旨在在时间窗口内找到(近乎)最优的轨迹集。为了处理可能的轨迹假设的巨大空间,通常通过某种形式的数据驱动或常规离散化将其缩减为有限集。在这项工作中,我们提出了一种多目标跟踪的替代方案,以尽量减少连续能量。与最近的方法相反,我们专注于设计一种与问题的更完整表示相对应的能量,而不是适合于全局优化的一种能量。除了图像证据外,能量函数还考虑了物理约束,例如目标动力学,互斥和跟踪持久性。此外,使用显式遮挡推理处理部分图像证据,并使用外观模型消除不同目标的歧义。但是,要找到建议的非凸能量的极小局部极小值,我们构建了一个合适的优化方案,该方案在连续共轭梯度下降和离散跨维跳变之间交替。执行这些移动以使其始终减少能量,从而使搜索能够逃脱弱小的最小值,并探索尺寸变化的搜索空间的很大一部分。我们通过对几个公共数据集进行广泛的定量评估,证明了我们方法的有效性。

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