activity
20182020
most citedLearning Monocular Visual Odometry via Self-Supervised Long-Term Modeling

3 citations · 4 across the 2 of their papers we have counts for

collaborators

8 papers

cs.CV20201 cited

Image Stitching and Rectification for Hand-Held Cameras

Bingbing Zhuang, Quoc-Huy Tran

In this paper, we derive a new differential homography that can account for the scanline-varying camera poses in Rolling Shutter (RS) cameras, and demonstrate its application to ca…

cs.CV20203 cited

Learning Monocular Visual Odometry via Self-Supervised Long-Term Modeling

Yuliang Zou, Pan Ji, Quoc-Huy Tran +2

Monocular visual odometry (VO) suffers severely from error accumulation during frame-to-frame pose estimation. In this paper, we present a self-supervised learning method for VO wi…

cs.CV2020

Towards Anomaly Detection in Dashcam Videos

Sanjay Haresh, Sateesh Kumar, M. Zeeshan Zia +1

Inexpensive sensing and computation, as well as insurance innovations, have made smart dashboard cameras ubiquitous. Increasingly, simple model-driven computer vision algorithms fo…

cs.CV2020

Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction

Lokender Tiwari, Pan Ji, Quoc-Huy Tran +3

Classical monocular Simultaneous Localization And Mapping (SLAM) and the recently emerging convolutional neural networks (CNNs) for monocular depth prediction represent two largely…

cs.CV2019

Degeneracy in Self-Calibration Revisited and a Deep Learning Solution for Uncalibrated SLAM

Bingbing Zhuang, Quoc-Huy Tran, Pan Ji +3

Self-calibration of camera intrinsics and radial distortion has a long history of research in the computer vision community. However, it remains rare to see real applications of su…

cs.LG2019

Sales Demand Forecast in E-commerce using a Long Short-Term Memory Neural Network Methodology

Kasun Bandara, Peibei Shi, Christoph Bergmeir +3

Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce fore…