| roflcoopter/viseron |
1,263 |
|
0 |
0 |
about 2 years ago |
0 |
|
86 |
mit |
Python |
| Self-hosted, local only NVR and AI Computer Vision software. With features such as object detection, motion detection, face recognition and more, it gives you the power to keep an eye on your home, office or any other place you want to monitor. |
| vantage-vision-vv/Anomaly-Detection-in-Surveillance-Videos |
147 |
|
0 |
0 |
over 7 years ago |
0 |
|
9 |
|
Python |
| Real-World Anomaly Detection in Surveillance Videos |
| norkator/open-intelligence |
125 |
|
0 |
0 |
over 3 years ago |
0 |
|
20 |
other |
TypeScript |
| Creepy stalking tool to process security camera motion triggered images and sort seen objects in different categories, detect license plates and faces. Has PWA ready web front end. Meant to make property monitoring faster without need to watch video recordings. |
| darpan-jain/crowd-counting-using-tensorflow |
79 |
|
0 |
0 |
about 3 years ago |
0 |
|
1 |
mit |
Python |
| Tensorflow implementation of crowd counting using CNNs from overhead surveillance cameras. |
| damien911224/theWorldInSafety |
31 |
|
0 |
0 |
almost 4 years ago |
0 |
|
8 |
gpl-3.0 |
C++ |
| Surveillance System Against Violence |
| MrSupiri/speculo |
20 |
|
0 |
0 |
almost 5 years ago |
0 |
|
10 |
mit |
Jupyter Notebook |
| Realtime face detection and recognition using deep learning |
| abdullahahsann/VideoEnhancementForSurviellance |
11 |
|
0 |
0 |
over 2 years ago |
0 |
|
0 |
gpl-3.0 |
|
| Video Enhancement For Surveillance |
| ArjunInventor/CogniTrack |
9 |
|
0 |
0 |
about 6 years ago |
0 |
|
3 |
mit |
Python |
| CogniTrack is an Artificial Intelligence powered person tracking system that acquires images from CCTV cameras and tracks individuals appearing in the frame in real-time. |
| jtn-ms/surveillance-system |
8 |
|
0 |
0 |
over 7 years ago |
0 |
|
|
other |
Jupyter Notebook |
| surveillance system based on video |
| seniorPro/Intelligent-Surveillance-System-for-Abandoned-Luggage |
7 |
|
0 |
0 |
about 8 years ago |
0 |
|
0 |
|
Python |
| This project combines the Computer Vision and Machine Learning algorithm in order to detect abandoned luggage. Faster R-CNN is used for object recognition. |
