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HOG vs YOLO Detector
Simeon ADEBOLA edited this page Aug 31, 2018
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While Histogram of Oriented Gradients(HOG) had previously been shown to be a useful and widely utilized approach to object detection, with the rise of convoluted neural networks and deep learning, we now have better methods for object detection.
One of such methods is YOLO (You Only Look Once) which is based on deep learning. OpenPTrack v2 "Gnocchi release" uses YOLO v2 and there are plans to implement YOLO v3 in the future.
Compared to HOG, YOLO is more accurate and much faster. However, YOLO requires more computational power.
- System Requirements
- Supported Hardware
- Initial Network Configuration
- Example Hardware List for UCLA Setup
- Making the Checkerboard
- Time Synchronization
- Pre-Tracking Configuration
- Camera Network Configuration
- Single Camera
- Setting Parameters
- Multi-Sensor Person Tracking
- HOG vs YOLO Detectors
- World Coordinate Settings
- Single Camera
- Pose Initialization
- Multi Sensor Pose Annotation
- Pose Best Practices
- Setting Parameters
- Single Camera
- Setting Parameters
- Multi Sensor Object Tracking
- YOLO Custom Training & Testing
- Yolo Trainer
- Single Camera
- Setting Parameters
- Multi Sensor Face Detection and Recognition
- Face Detection and Recognition Data Format
How to receive tracking data in: