Search Results for author:
Found 3 papers, 0 papers with code
Background Subtraction with Real-time Semantic Segmentation
Our proposed framework consists of two components, a traditional BGS segmenter $\mathcal{B}$ and a real-time semantic segmenter $\mathcal{S}$.
A Robust Local Binary Similarity Pattern for Foreground Object Detection
First, we propose a robust texture operator named Robust Local Binary Similarity Pattern (RLBSP), which shows strong robustness to illumination variations and dynamic backgrounds.
Combining Background Subtraction Algorithms with Convolutional Neural Network
Accurate and fast extraction of foreground object is a key prerequisite for a wide range of computer vision applications such as object tracking and recognition.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
