| GeekAlexis/FastMOT |
786 |
|
0 |
0 |
about 4 years ago |
0 |
|
15 |
mit |
Python |
| High-performance multiple object tracking based on YOLO, Deep SORT, and KLT 🚀 |
| ltkong218/IFRNet |
191 |
|
0 |
0 |
over 2 years ago |
0 |
|
25 |
mit |
Python |
| IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation (CVPR 2022) |
| bryanyzhu/Hidden-Two-Stream |
168 |
|
0 |
0 |
over 8 years ago |
0 |
|
2 |
other |
C++ |
| Caffe implementation for "Hidden Two-Stream Convolutional Networks for Action Recognition" |
| ltkong218/FastFlowNet |
165 |
|
0 |
0 |
about 3 years ago |
0 |
|
14 |
mit |
Python |
| FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation (ICRA 2021) |
| keeffEoghan/glsl-optical-flow |
46 |
|
0 |
2 |
over 3 years ago |
22 |
October 11, 2022 |
0 |
mit |
GLSL |
| Optical flow shader for WebGL - BYORenderer. |
| gabrieleilertsen/briefmatch |
32 |
|
0 |
0 |
over 8 years ago |
0 |
|
1 |
bsd-3-clause |
C++ |
| BriefMatch real-time GPU optical flow |
| aoso3/Real-Time-Abnormal-Events-Detection-and-Tracking-in-Surveillance-System |
30 |
|
0 |
0 |
over 4 years ago |
0 |
|
1 |
mit |
C# |
| The main abnormal behaviors that this project can detect are: Violence, covering camera, Choking, lying down, Running, Motion in restricted areas. It provides much flexibility by allowing users to choose the abnormal behaviors they want to be detected and keeps track of every abnormal event to be reviewed. We used three methods to detect abnormal behaviors: Motion influence map, Pattern recognition models, State event model. For multi-camera tracking, we combined a single camera tracking algorithm with a spatial based algorithm. |
| hsp-iit/roft |
27 |
|
0 |
0 |
almost 3 years ago |
0 |
|
0 |
gpl-2.0 |
C++ |
| Real-time Optical Flow-aided 6D Object Pose and Velocity Tracking |
| heudiasyc/rt_of_low_high_res_event_cameras |
18 |
|
0 |
0 |
about 2 years ago |
0 |
|
1 |
other |
C++ |
| Code for the "Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras" article |
