1. A Tutorial on HOG Human Detection
Yen-Chun Chen

2. They use HOG human detector
So far we have seen...
Action detection
Deformable Part-Based Model
They use HOG human detector

3. Histograms of Oriented Gradients
Dalal and Triggs, Histograms of Oriented Gradients for Human Detection, CVPR05

4. Challenges in person detection
Different poses
Variable appearance/clothing
Other difficulties common in general object detections
illumination
background clutter

5. Overview
64 x 128 detection window
8 x 8 cell

6. Gradient histogram 8 x 8 cell -- 64 gradients 9-bin histogram 85 3/4
1/4
3/4

7. Block Normalization
Increase robustness to contrast, illumination changes
Group cells into overlapping blocks
normalize the block as 36-d (4 histograms x 9 bins) vector

8. Final Descriptor and Detection
64 x 128 window -- 7 x 15 blocks
7 x 15 blocks x 4 cells x 9-bin histogram = 3780-d feature vectors
Use linear SVM to classify person/ non-person windows
Use sliding window in detection

9. Engineering the Feature

10. Color space

11. Color space different color spaces don’t affect much
color better than greyscale

12. Gradients Gaussian smoothing Different derivative masks
no smoothing !!
the information of the image is from abrupt edges at fine scale

13. Spatial/ Orientation Binning
9-bin works the best
signed/ unsigned gradient?
(360 degrees vs 180 degrees)
The clothing of human and the background varies a lot
Make the signs of contrast uninformative

14. Evaluation Metric
Detection, a single accuracy value doesn’t make sense
Low threshold- detect more people but many false positives
High threshold- fewer false positives but doesn’t detect all people
Detection Error Tradeoff (DET) Curves
Miss Rate = (false negative) / (true positive - false negative)
FPPW (False Positive Per Window) :
(false positive) / (total # of negative training sample)

15. The Rest
faster computation

16. Discussion Normalization local variations in illumination and
foreground-background contrast make gradients vary a lot
overlap significantly improves the performance

17. Most important cells are on contours of human shape
Gradients inside the person are negative cues

18. Demo