Fitting, Comparison, and Alignment of Trajectories on Positive Semi-Definite Matrices with Application to Action Recognition

Video-Based Action Recognition Using Rate-Invariant Analysis of Covariance Trajectories

Statistical classification of actions in videos is mostly performed by extracting relevant features, particularly covariance features, from image frames and studying time series associated with temporal evolutions of these features. A natural mathematical representation of activity videos is in form of parameterized trajectories on the covariance manifold, i.e. the set of symmetric, positive-definite matrices (SPDMs). The variable execution-rates of actions implies variable parameterizations of the resulting trajectories, and complicates their classification. Since action classes are invariant…

基于鲁棒模型拟合的事件数据关联用于基于事件的目标跟踪

Event Data Association via Robust Model Fitting for Event-based Object Tracking

本文提出一种新颖的事件数据关联（EDA）方法，通过基于信息熵的异步融合、确定性模型假设生成与两阶段加权算法解决事件关联与融合问题，并在目标跟踪任务中验证了其在高速、运动模糊及高动态范围等挑战场景下的有效性。

Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices

We propose an automatic method to estimate self-reported pain based on facial landmarks extracted from videos. For each video sequence, we decompose the face into four different regions and the pain intensity is measured by modeling the dynamics of facial movement using the landmarks of these regions. A formulation based on Gram matrices is used for representing the trajectory of landmarks on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. A curve fitting algorithm is used to smooth the trajectories and temporal alignment is performed to compute the similari…

将动态脑功能连接作为协方差矩阵空间上的轨迹进行分析

Analyzing Dynamical Brain Functional Connectivity As Trajectories on Space of Covariance Matrices

本研究将脑功能连接动态表示为对称正定矩阵空间轨迹，提出基于度量的降维方法，在HCP数据集上实现了与前沿水平相当或更优的任务分类效果。

Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics

We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory representations on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. Curve fitting and temporal alignment were then used to smooth the extracted trajectories. A Support Vector Regression model was then trained to encode the extracted trajectories into ten pain intensity levels consistent with the Visual Analogue Scale fo…