https://github.com/grantgasser/vehicle-detection
Detecting and tracking vehicles with traditional computer vision techniques as well as deep learning
https://github.com/grantgasser/vehicle-detection
computer-vision feature-extraction vehicle-detection yolov5
Last synced: 3 months ago
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Detecting and tracking vehicles with traditional computer vision techniques as well as deep learning
- Host: GitHub
- URL: https://github.com/grantgasser/vehicle-detection
- Owner: grantgasser
- License: mit
- Created: 2020-12-03T22:35:53.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-02-10T02:58:16.000Z (over 4 years ago)
- Last Synced: 2025-02-08T18:14:35.237Z (5 months ago)
- Topics: computer-vision, feature-extraction, vehicle-detection, yolov5
- Language: Python
- Homepage:
- Size: 5.88 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Vehicle Detection and Tracking
## Final Results via Deep Learning
Click to play video:
[](https://youtu.be/sitoSZPr8HQ)
## 1st Implementation: Manual Feature Extraction + Classifier
1. Color Features
- Color Histogram
- Color Spaces (HSV, LUV, etc.)
- Spatial Binning (`cv2.resize()`)
2. Gradient Features
- Histogram of Oriented Gradients (HOG)
### Feature Extraction
- Normalize each feature, then combine into one feature vector
- Train-Test split
- Could even use a decision tree for feature selection
- Be careful of time dependencies, even w/ a random train-test split
### Train Vehicle Classifier
Train SVM to classify Car/Not Car.
### Sliding Window
Slide a window (at different scales) over the frames and for each window, classify car/not-car.
### Manual Feature Extraction Results
Click to play video:
[](https://youtu.be/N94dRl46f8k)
## Deep Learning Implementation (YOLOv5)
Learning the features directly.
Click to play video:
[](https://youtu.be/sitoSZPr8HQ)