| JunshengFu/vehicle-detection |
509 |
|
0 |
0 |
almost 4 years ago |
0 |
|
20 |
gpl-3.0 |
Python |
| Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG). |
| udacity/CarND-Vehicle-Detection |
239 |
|
0 |
0 |
over 4 years ago |
0 |
|
2 |
mit |
Shell |
| Vehicle Detection Project |
| diyjac/SDC-P5 |
159 |
|
0 |
0 |
over 7 years ago |
0 |
|
3 |
mit |
Python |
| Udacity Self-Driving Car Project 5: Vehicle Detection and Tracking |
| bingrao/vehicle-detection |
16 |
|
0 |
0 |
over 8 years ago |
0 |
|
0 |
other |
Python |
| Record the speed of cars passing in front of the Raspberry Pi Picamera. Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG). |
| ksketo/CarND-Vehicle-Detection |
10 |
|
0 |
0 |
almost 7 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Vehicle detection implemented with the SSD network |
| pkern90/vehicle-tracking |
7 |
|
0 |
0 |
about 9 years ago |
0 |
|
0 |
|
Jupyter Notebook |
| Vehicle tracking using computer vision |
