| niais/Awesome-Skeleton-based-Action-Recognition |
618 |
|
0 |
0 |
almost 3 years ago |
0 |
|
4 |
|
HTML |
| Skeleton-based Action Recognition |
| martinruenz/maskfusion |
318 |
|
0 |
0 |
about 6 years ago |
0 |
|
13 |
other |
C++ |
| MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects |
| kchengiva/Shift-GCN |
173 |
|
0 |
0 |
over 4 years ago |
0 |
|
3 |
other |
Python |
| The implementation for "Skeleton-Based Action Recognition with Shift Graph Convolutional Network" (CVPR2020 oral). |
| cagbal/Skeleton-Based-Action-Recognition-Papers-and-Notes |
105 |
|
0 |
0 |
over 5 years ago |
0 |
|
4 |
|
|
| Skeleton-based Action Recognition Papers and Small Notes and Top 2 Leaderboard for NTU-RGBD |
| jongmoochoi/irisfaceRGBD |
75 |
|
0 |
0 |
about 8 years ago |
0 |
|
6 |
|
C |
| 3D face modeling and recognition using a depth camera (RGBD) |
| paroj/ObjRecPoseEst |
64 |
|
0 |
0 |
almost 7 years ago |
0 |
|
2 |
|
Python |
| Object Detection and 3D Pose Estimation |
| kchengiva/DecoupleGCN-DropGraph |
21 |
|
0 |
0 |
over 5 years ago |
0 |
|
1 |
other |
Python |
| The implementation for "Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition" (ECCV2020). |
| acaglayan/exploitCNN-RNN |
11 |
|
0 |
0 |
about 4 years ago |
0 |
|
2 |
|
MATLAB |
| Exploiting Multi-Layer Features Using a CNN-RNN Approach for RGB-D Object Recognition |
| AndreBrasUC/Object_Recognition_From_RGBD_Data |
9 |
|
0 |
0 |
almost 8 years ago |
0 |
|
0 |
|
Matlab |
| In recent years, object recognition has attracted increasing attention of researchers due to its numerous applications. For instance, object recognition enables collaborative robots to carry out tasks like searching for an object in an unstructured environment or retrieving a tool for a human coworker. In this study, we present a new technique for unsupervised feature extraction from red, green, blue, plus depth (RGB-D) data, which is then combined with several classifiers to perform object recognition. Specifically, our architecture first segments all objects in a table top scene through an unsupervised clustering technique. Then, it focuses separately on each object to extract both shape and visual features. We conduct experiments on a subset of 20 objects selected from the YCB object and model set and evaluate the performance of several classifiers. |
| kevinlisun/romans_stack |
7 |
|
0 |
0 |
over 5 years ago |
0 |
|
2 |
|
C++ |
| This is the Vision System (Object Dection & Recognition) for EU H2020 project RoMaNs |
