Motion detection has been widely used in many applications, such as
surveillance and robotics. Due to the presence of the static background, a
motion video can be decomposed into a low-rank background and a sparse
foreground. Many regularization techniques that preserve low-rankness of
matrices can therefore be imposed on the background. In the meanwhile,
geometry-based regularizations, such as graph regularizations, can be imposed
on the foreground. Recently, weighted regularization techniques including the
weighted nuclear norm regularization have been proposed in the image processing
community to promote adaptive sparsity while achieving efficient performance.
In this paper, we propose a robust dual graph regularized moving object
detection model based on a novel weighted nuclear norm regularization and
spatiotemporal graph Laplacians. Numerical experiments on realistic human
motion data sets have demonstrated the effectiveness and robustness of this
approach in separating moving objects from background, and the enormous
potential in robotic applications.

本文提出了一个基于加权核范数正则化和时空图拉普拉斯算子的双重图正则化移动对象检测模型，在实际的人体运动数据集上进行了数值实验，证明了该方法在分离运动物体和背景方面的有效性和稳健性，并展示了在机器人应用中的巨大潜力。

基于双图和加权核范数正则化的人体运动检测

Human Motion Detection Based on Dual-Graph and Weighted Nuclear Norm  Regularizations

Deep Neural Networks (DNNs) have been widely used in software making
decisions impacting people's lives. However, they have been found to exhibit
severe erroneous behaviors that may lead to unfortunate outcomes. Previous work
shows that such misbehaviors often occur due to class property violations
rather than errors on a single image. Although methods for detecting such
errors have been proposed, fixing them has not been studied so far. Here, we
propose a generic method called Weighted Regularization (WR) consisting of five
concrete methods targeting the error-producing classes to fix the DNNs. In
particular, it can repair confusion error and bias error of DNN models for both
single-label and multi-label image classifications. A confusion error happens
when a given DNN model tends to confuse between two classes. Each method in WR
assigns more weights at a stage of DNN retraining or inference to mitigate the
confusion between target pair. A bias error can be fixed similarly. We evaluate
and compare the proposed methods along with baselines on six widely-used
datasets and architecture combinations. The results suggest that WR methods
have different trade-offs but under each setting at least one WR method can
greatly reduce confusion/bias errors at a very limited cost of the overall
performance.

提出一种名为加权正则化的通用方法，通过调整深度神经网络的权重来修复图像分类中的混淆错误和偏差错误，并在广泛使用的六个数据集和结构组合上进行了评估和比较。