Search Results for author:
Found 13 papers, 7 papers with code
BabelCalib: A Universal Approach to Calibrating Central Cameras
A lot of the difficulties of general camera calibration lie in the use of a forward projection model.
Adapting Grad-CAM for Embedding Networks
Then, we develop an efficient weight-transfer method to explain decisions for any image without back-propagation.
Sports Camera Calibration via Synthetic Data
Here we propose a highly automatic method for calibrating sports cameras from a single image using synthetic data.
Learning Sports Camera Selection from Internet Videos
This work addresses camera selection, the task of predicting which camera should be "on air" from multiple candidate cameras for soccer broadcast.
A Two-point Method for PTZ Camera Calibration in Sports
We also propose a fast random forest method to predict pan-tilt angles without image-to-image feature matching, leading to an efficient calibration method for new images.
Backtracking Regression Forests for Accurate Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection.
Light Cascaded Convolutional Neural Networks for Accurate Player Detection
Vision based player detection is important in sports applications.
Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees
We study the problem of online prediction for realtime camera planning, where the goal is to predict smooth trajectories that correctly track and frame objects of interest (e. g., players in a basketball game).
Convex Factorization Machine for Regression
We propose the convex factorization machine (CFM), which is a convex variant of the widely used Factorization Machines (FMs).
Optimal Stochastic Strongly Convex Optimization with a Logarithmic Number of Projections
We consider stochastic strongly convex optimization with a complex inequality constraint.
Clustered Multi-Task Learning Via Alternating Structure Optimization
We further establish the equivalence relationship between the proposed convex relaxation of CMTL and an existing convex relaxation of ASO, and show that the proposed convex CMTL formulation is significantly more efficient especially for high-dimensional data.
Efficient Recovery of Jointly Sparse Vectors
MMV is an extension of the single measurement vector (SMV) model employed in standard compressive sensing (CS).
