1. Jiahe Li

2. Detection examples

3. Overview

4. Network architecture

5. Notation
Input
Output

6. Sample collection

7. Positive sample

8. Negative sample

9. Loss function

10. Datasets Caltech CityPersons 11 sets of videos: Train (6), Test (5)
Reasonable ([0, 0.35]), Partial ([0.01, 0.35]), Heavy ([0.36, 0.8])
CityPersons
Train (2975), Val (500) and Test (1575)
Reasonalbe, Small ([50,75], [0,0.35]), Heavy ([50), [0.35,0.8]), All ([20), [0, 0.8])

11. Evaluation metric Log-average miss rate
Averaging miss rates at 9 false positives per image (FPPI) point sevenly spaced between 10−2 and 1 in log space

12. Fast R-CNN

13. Faster R-CNN

14. Experimental results

15. Experimental results

16. Experimental results

17. Experimental results

18. Conclusion A bi-box regression approach A training strategy
Full body estimation
Visible part estimation
A training strategy
A new criterion
Positive sample