- 01. Intro to Vehicle Tracking
- 02. Arpan and Drew
- 03. Finding Cars
- 04. Object Detection Overview
- 05. Manual Vehicle Detection
- 06. Features
- 07. Feature Intuition
- 08. Color Features
- 09. Template Matching
- 10. Template Matching Quiz
- 11. Color Histogram Features
- 12. Histograms of Color
- 13. Histogram Comparison
- 14. Color Spaces
- 15. Explore Color Spaces
- 16. Spatial Binning of Color
- 17. Gradient Features
- 18. HOG Features
- 19. Data Exploration
- 20. scikit-image HOG
- 21. Combining Features
- 22. Combine and Normalize Features
- 23. Build a Classifier
- 24. Labeled Data
- 25. Data Preparation
- 26. Train a Classifier
- 27. Parameter Tuning
- 28. Color Classify
- 29. HOG Classify
- 30. Sliding Windows
- 31. How many windows?
- 32. Sliding Window Implementation
- 33. Multi-scale Windows
- 34. Search and Classify
- 35. Hog Sub-sampling Window Search
- 36. False Positives
- 37. Multiple Detections & False Positives
- 38. Tracking Pipeline
- 39. Summary
- 40. Traditional vs. Deep Learning Approach