Deformable parts models show a great potential in tracking by principally
addressing non-rigid object deformations and self occlusions, but according to
recent benchmarks, they often lag behind the holistic approaches. The reason is
that potentially large number of degrees of freedom have to be estimated for
object localization and simplifications of the constellation topology are often
assumed to make the inference tractable. We present a new formulation of the
constellation model with correlation filters that treats the geometric and
visual constraints within a single convex cost function and derive a highly
efficient optimization for MAP inference of a fully-connected constellation. We
propose a tracker that models the object at two levels of detail. The coarse
level corresponds a root correlation filter and a novel color model for
approximate object localization, while the mid-level representation is composed
of the new deformable constellation of correlation filters that refine the
object location. The resulting tracker is rigorously analyzed on a highly
challenging OTB, VOT2014 and VOT2015 benchmarks, exhibits a state-of-the-art
performance and runs in real-time.

我们提出了一种新的 constellation 模型配合相关滤波器的公式，用于追踪目标并处理非刚性物体变形和自遮挡问题。我们设计了两个级别的模型来逼近目标位置，经过高难度测试表明本方法能达到最先进表现且实时运行。

可变形部件相关滤波器用于鲁棒视觉跟踪

Deformable Parts Correlation Filters for Robust Visual Tracking

Deformable Parts Models and Convolutional Networks each have achieved notable
performance in object detection. Yet these two approaches find their strengths
in complementary areas: DPMs are well-versed in object composition, modeling
fine-grained spatial relationships between parts; likewise, ConvNets are adept
at producing powerful image features, having been discriminatively trained
directly on the pixels. In this paper, we propose a new model that combines
these two approaches, obtaining the advantages of each. We train this model
using a new structured loss function that considers all bounding boxes within
an image, rather than isolated object instances. This enables the non-maximal
suppression (NMS) operation, previously treated as a separate post-processing
stage, to be integrated into the model. This allows for discriminative training
of our combined Convnet + DPM + NMS model in end-to-end fashion. We evaluate
our system on PASCAL VOC 2007 and 2011 datasets, achieving competitive results
on both benchmarks.

本文提出了一种将 Deformable Parts Models 和 Convolutional Networks 相结合的新模型，并使用结构化损失函数进行训练，使得之前作为单独后处理步骤的 NMS 操作成为了模型的一部分，从而使得我们的系统在 PASCAL VOC 2007 和 2011 数据集上实现了竞争性的结果。