Developing a robust object tracker is a challenging task due to factors such
as occlusion, motion blur, fast motion, illumination variations, rotation,
background clutter, low resolution and deformation across the frames. In the
literature, lots of good approaches based on sparse representation have already
been presented to tackle the above problems. However, most of the algorithms do
not focus on the learning of sparse representation. They only consider the
modeling of target appearance and therefore drift away from the target with the
imprecise training samples. By considering all the above factors in mind, we
have proposed a visual object tracking algorithm by integrating a
coarse-to-fine search strategy based on sparse representation and the weighted
multiple instance learning (WMIL) algorithm. Compared with the other trackers,
our approach has more information of the original signal with less complexity
due to the coarse-to-fine search method, and also has weights for important
samples. Thus, it can easily discriminate the background features from the
foreground. Furthermore, we have also selected the samples from the un-occluded
sub-regions to efficiently develop the strong classifier. As a consequence, a
stable and robust object tracker is achieved to tackle all the aforementioned
problems. Experimental results with quantitative as well as qualitative
analysis on challenging benchmark datasets show the accuracy and efficiency of
our method.

通过整合基于稀疏表示的粗粒度到细粒度搜索策略和加权多实例学习算法，我们提出了一种稳定且鲁棒的目标跟踪算法，用于解决遮挡、运动模糊、快速运动、光照变化、旋转、背景杂乱、低分辨率和帧间形变等问题，并通过在具有权重的重要样本上区分前景和背景特征，高效地开发了强分类器。实验结果表明，我们的方法在具有挑战性的基准数据集上进行了定量和定性分析，显示了其准确性和效率。

鲁棒压缩跟踪的在线加权多示例学习

Robust compressive tracking via online weighted multiple instance  learning

Lane graph estimation is a long-standing problem in the context of autonomous
driving. Previous works aimed at solving this problem by relying on
large-scale, hand-annotated lane graphs, introducing a data bottleneck for
training models to solve this task. To overcome this limitation, we propose to
use the motion patterns of traffic participants as lane graph annotations. In
our AutoGraph approach, we employ a pre-trained object tracker to collect the
tracklets of traffic participants such as vehicles and trucks. Based on the
location of these tracklets, we predict the successor lane graph from an
initial position using overhead RGB images only, not requiring any human
supervision. In a subsequent stage, we show how the individual successor
predictions can be aggregated into a consistent lane graph. We demonstrate the
efficacy of our approach on the UrbanLaneGraph dataset and perform extensive
quantitative and qualitative evaluations, indicating that AutoGraph is on par
with models trained on hand-annotated graph data. Model and dataset will be
made available at redacted-for-review.

提出了使用交通参与者的运动模式作为车道图注释的自动车道图方法，通过预训练的对象跟踪器和 RGB 图像，可以在不需要人类监督的情况下预测车道图，实验证明效果与手动注释的图形数据训练的模型相当。

AutoGraph：从交通观测中预测车道图

AutoGraph: Predicting Lane Graphs from Traffic Observations

In recent years, assessing action quality from videos has attracted growing
attention in computer vision community and human computer interaction. Most
existing approaches usually tackle this problem by directly migrating the model
from action recognition tasks, which ignores the intrinsic differences within
the feature map such as foreground and background information. To address this
issue, we propose a Tube Self-Attention Network (TSA-Net) for action quality
assessment (AQA). Specifically, we introduce a single object tracker into AQA
and propose the Tube Self-Attention Module (TSA), which can efficiently
generate rich spatio-temporal contextual information by adopting sparse feature
interactions. The TSA module is embedded in existing video networks to form
TSA-Net. Overall, our TSA-Net is with the following merits: 1) High
computational efficiency, 2) High flexibility, and 3) The state-of-the art
performance. Extensive experiments are conducted on popular action quality
assessment datasets including AQA-7 and MTL-AQA. Besides, a dataset named Fall
Recognition in Figure Skating (FR-FS) is proposed to explore the basic action
assessment in the figure skating scene.

本文提出了一种基于 Tube Self-Attention Network 的方法来评估视频中行动的质量，并通过引入单个对象跟踪器和 Tube Self-Attention Module 来提取丰富的时空上下文信息。此外，在 AQA-7, MTL-AQA 数据集上进行广泛实验，并提出了一个名为 FR-FS 的数据集以探究花样滑冰场景中的基本行动评估。