In this paper, we present a novel method based on online target-specific
metric learning and coherent dynamics estimation for tracklet (track fragment)
association by network flow optimization in long-term multi-person tracking.
Our proposed framework aims to exploit appearance and motion cues to prevent
identity switches during tracking and to recover missed detections.
Furthermore, target-specific metrics (appearance cue) and motion dynamics
(motion cue) are proposed to be learned and estimated online, i.e. during the
tracking process. Our approach is effective even when such cues fail to
identify or follow the target due to occlusions or object-to-object
interactions. We also propose to learn the weights of these two tracking cues
to handle the difficult situations, such as severe occlusions and
object-to-object interactions effectively. Our method has been validated on
several public datasets and the experimental results show that it outperforms
several state-of-the-art tracking methods.

本文提出一种基于在线目标特定度量学习和协同动力学估计的方法，通过网络流优化来实现长期多人跟踪中的轨迹相关性关联。该方法利用外观和动态线索，避免跟踪时出现身份混淆，并恢复漏检。在跟踪过程中，学习和在线估计目标特定的度量（外观线索）和动态（运动线索），即使由于遮挡或对象间相互作用而无法识别或跟踪目标时仍有效。文中还提出了学习这两种跟踪线索的权重来有效处理严重遮挡和对象间相互作用等困难情况。该方法在多个公共数据集上验证，实验结果表明优于多种最先进的跟踪方法。

在线目标特定度量学习和连贯动力学估计进行路径片段关联

Tracklet Association by Online Target-Specific Metric Learning and  Coherent Dynamics Estimation

We consider a class of learning problems that involve a structured
sparsity-inducing norm defined as the sum of $\ell_\infty$-norms over groups of
variables. Whereas a lot of effort has been put in developing fast optimization
methods when the groups are disjoint or embedded in a specific hierarchical
structure, we address here the case of general overlapping groups. To this end,
we show that the corresponding optimization problem is related to network flow
optimization. More precisely, the proximal problem associated with the norm we
consider is dual to a quadratic min-cost flow problem. We propose an efficient
procedure which computes its solution exactly in polynomial time. Our algorithm
scales up to millions of variables, and opens up a whole new range of
applications for structured sparse models. We present several experiments on
image and video data, demonstrating the applicability and scalability of our
approach for various problems.

本文研究了一个结构化稀疏正则化学习问题，提出了一种基于网络流优化的高效算法，在多个领域展示出了它的可行性和可扩展性。

用于结构稀疏性的网络流算法

Network Flow Algorithms for Structured Sparsity

The network flow optimization approach is offered for restoration of
grayscale and color images corrupted by noise. The Ising models are used as a
statistical background of the proposed method. The new multiresolution network
flow minimum cut algorithm, which is especially efficient in identification of
the maximum a posteriori estimates of corrupted images, is presented. The
algorithm is able to compute the MAP estimates of large size images and can be
used in a concurrent mode. We also describe the efficient solutions of the
problem of integer minimization of two energy functions for the Ising models of
gray-scale and color images.

提出了一种网络流优化方法，用于恢复受噪声干扰的灰度和彩色图像，其中使用了 Ising 模型作为所提出方法的统计背景，并呈现了特别有效的多分辨率网络流最小割算法，该算法能够计算出大尺寸图像的最大后验估计，并且能够以并发模式运行。同时还讨论了 Ising 模型灰度和彩色图像两个能量函数的整数最小化问题的高效解决方案。