In recent years, Video Object Segmentation (VOS) has emerged as a
complementary method to Video Object Tracking (VOT). VOS focuses on classifying
all the pixels around the target, allowing for precise shape labeling, while
VOT primarily focuses on the approximate region where the target might be.
However, traditional segmentation modules usually classify pixels frame by
frame, disregarding information between adjacent frames.
In this paper, we propose a new algorithm that addresses this limitation by
analyzing the motion pattern using the inherent tensor structure. The tensor
structure, obtained through Tucker2 tensor decomposition, proves to be
effective in describing the target's motion. By incorporating this information,
we achieved competitive results on Four benchmarks LaSOT\cite{fan2019lasot},
AVisT\cite{noman2022avist}, OTB100\cite{7001050}, and
GOT-10k\cite{huang2019got} LaSOT\cite{fan2019lasot} with SOTA. Furthermore, the
proposed tracker is capable of real-time operation, adding value to its
practical application.

通过分析运动模式使用固有的张量结构进行片段分割和目标追踪，该算法在 LaSOT、AVisT、OTB100 和 GOT-10k 等基准测试中取得了有竞争力的结果，并能够实现实时操作。

运动分析的鲁棒视觉跟踪

Robust Visual Tracking by Motion Analyzing

Time-stamp aware anomaly detection in traffic videos is an essential task for
the advancement of the intelligent transportation system. Anomaly detection in
videos is a challenging problem due to sparse occurrence of anomalous events,
inconsistent behavior of a different type of anomalies and imbalanced available
data for normal and abnormal scenarios. In this paper, we present a three-stage
pipeline to learn the motion patterns in videos to detect a visual anomaly.
First, the background is estimated from recent history frames to identify the
motionless objects. This background image is used to localize the
normal/abnormal behavior within the frame. Further, we detect an object of
interest in the estimated background and categorize it into anomaly based on a
time-stamp aware anomaly detection algorithm. We also discuss the challenges
faced in improving performance over the unseen test data for traffic anomaly
detection. Experiments are conducted over Track 3 of NVIDIA AI city challenge
2019. The results show the effectiveness of the proposed method in detecting
time-stamp aware anomalies in traffic/road videos.

本文提出了一种三阶段管道模型，通过学习视频中的运动模式来检测视觉异常，并使用时间戳感知的异常检测算法将目标物体分类为异常，实验结果表明该方法能够有效检测交通路况视频中的时间戳感知异常。

交通视频中时间戳感知异常检测的挑战

Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos

The image blurring process is generally modelled as the convolution of a blur
kernel with a latent image. Therefore, the estimation of the blur kernel is
essentially important for blind image deblurring. Unlike existing approaches
which focus on approaching the problem by enforcing various priors on the blur
kernel and the latent image, we are aiming at obtaining a high quality blur
kernel directly by studying the problem in the frequency domain. We show that
the auto-correlation of the absolute phase-only image can provide faithful
information about the motion (e.g. the motion direction and magnitude, we call
it the motion pattern in this paper.) that caused the blur, leading to a new
and efficient blur kernel estimation approach. The blur kernel is then refined
and the sharp image is estimated by solving an optimization problem by
enforcing a regularization on the blur kernel and the latent image. We further
extend our approach to handle non-uniform blur, which involves spatially
varying blur kernels. Our approach is evaluated extensively on synthetic and
real data and shows good results compared to the state-of-the-art deblurring
approaches.

本文提出了一种基于自相关的相位图像的频域方法，直接从运动模式中提取高质量的模糊核，在加入对模糊核和潜在图像的正则化约束下，优化求解模糊核和清晰图像的问题，从而实现图像去模糊。此方法可处理空间变化和非均匀模糊，并在合成和真实数据上得到了良好的结果。