OpenTalks.AI - Дмитрий Пагин, Fast cars detection and traffic estimation​

Transcript

1. Fast cars detection and traffic estimation Dmitriy Pagin, ML and

   CV developer

2. Task Road traffic analysis in Russia is manual. It takes

   more than 8 hours for 15 minutes video today

3. Task • detect cars

4. Task • detect cars • track cars

5. Baseline - people tracking

6. Problems Cars: - faster (2 metres per frame!) - smaller

   (10 px in minimal dimension) + more predictable movement

7. YOLOv2 - blinking - problems on small cars - problems

   on edges

8. YOLOv2 1 fps

9. YOLOv3 - bigger + accurate on small + fullHD frame

   + robust

10. YOLOv3 7 fps

11. > 70k cars on 4k images Dataset

12. better than 1024x1024x1 Learning and Fine-tuning - 608x608 px -

    batchSize = 3 - custom augmenters
13. None

14. Learning and Fine-tuning - 608x608 px - batchSize = 3

    - custom augmenters - Radam optimizer (instead warmup + reduce LR) - Hard negative mining for trucks

15. Learning and Fine-tuning - 608x608 px - batchSize = 3

    - custom augmenters - Radam optimizer (instead warmup + reduce LR) - Hard negative mining for trucks mAP75 = 0.96

16. Baseline Inference Speed 7 fps

17. Weights Pruning

18. Weights Pruning -25% convs = size: 240 mb mAp: 0.9656

    inf: 150 ms size: 155 mb mAp: 0.9622 inf: 100 ms 10 fps

19. OpticalFlow step or classical cv is alive ! - find

    good features to track - calculate sparse optical flow

20. OpticalFlow step 19 fps Calculation doesnt work for 3 consistent

    frames

21. Speed extrapolation step - estimate speed as pixels/frame - extrapolate

    next position 28 fps

22. Final pipeline 1 2 3 4 5 6 Update trajectories

    4 5 6 step 1 step 2 Speed Extrapolation OpticalFlow YOLOv3 Detection Engine

23. 1 fps -> 28 fps on FULLHD

24. Tracking - IoU - Color descriptor (it’s enough!)

25. Bridges! - Allowed zone by motion vector - Size overlap

    - Color descriptor

26. Bridges! - Allowed zone by motion vector - Size overlap

    - Color descriptor

27. Thanks! Questions? dm.pagin@gmail.com +7 952 335 65 70

28. Appendix. Examples

29. Appendix. Examples

30. Appendix. Examples

31. Appendix. Yolov3

32. Weights Pruning Шаг mAP75 Число параметров, млн Размер сети, мб

    От изначальной, % Время прогона, мс Условие обрезания 0 0.965 60 241 100 150 - 1 0.962 55 218 91 140 5% от всех 2 0.962 50 197 83 132 5% от всех 3 0.963 39 155 64 112 15% для слоев с 400+ сверток 4 0.955 31 124 51 100 10% для слоев с 100+ сверток

33. Appendix. Radam

34. Pruning convs

35. Pruning convs. Good choice 2000

36. Pruning convs. Bad choice 25

37. Pruning flat