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Found 9 papers, 0 papers with code
From Image to Stability: Learning Dynamics from Human Pose
We propose and validate two end-to-end deep learning architectures to learn foot pressure distribution maps (dynamics) from 2D or 3D human pose (kinematics).
A Light-Weight Contrastive Approach for Aligning Human Pose Sequences
We present a simple unsupervised method for learning an encoder mapping short 3D pose sequences into embedding vectors suitable for sequence-to-sequence alignment by dynamic time warping.
Image-based Stability Quantification
Quantitative evaluation of human stability using foot pressure/force measurement hardware and motion capture (mocap) technology is expensive, time consuming, and restricted to the laboratory.
From Kinematics To Dynamics: Estimating Center of Pressure and Base of Support from Video Frames of Human Motion
To gain an understanding of the relation between a given human pose image and the corresponding physical foot pressure of the human subject, we propose and validate two end-to-end deep learning architectures, PressNet and PressNet-Simple, to regress foot pressure heatmaps (dynamics) from 2D human pose (kinematics) derived from a video frame.
Learning Dynamics from Kinematics: Estimating 2D Foot Pressure Maps from Video Frames
In biomechanics, Center of Pressure (CoP) is used in studies of human postural control and gait.
Regularity-Driven Facade Matching Between Aerial and Street Views
We present an approach for detecting and matching building facades between aerial view and street-view images.
Multi-target Tracking by Lagrangian Relaxation to Min-cost Network Flow
We propose a method for global multi-target tracking that can incorporate higher-order track smoothness constraints such as constant velocity.
Tracking Sports Players with Context-Conditioned Motion Models
Instead, we introduce a set of Game Context Features extracted from noisy detections to describe the current state of the match, such as how the players are spatially distributed.
Optimized Pedestrian Detection for Multiple and Occluded People
We present a quadratic unconstrained binary optimization (QUBO) framework for reasoning about multiple object detections with spatial overlaps.
