Here i will telling about methods in computer vision that used for creating road traffic analytics, and why computer vision is thee next step in analytics from hardware sensors.

1. KAI VISION
TRAFFIC ANALYTIC SYSTEM
Making city better
with accurate analytics

2. USING COMPUTER VISION
FOR ROAD TRAFFIC
ANALYTICS

3. DOWNLOAD SLIDES AT:
goo.gl / wsjNW3

4. PLAN
WHY DO WE NEED COMPUTER VISION?
NON DEEP LEARNING TECHNIQUES
DEEP LEARNING TECHNIQUES
WHATS NEXT?

5. WHY DO WE NEED
COMPUTER VISION?

6. BEFORE
PROS
- Simple usage
- Fast training with minimum
data
- High accuracy in ideal
conditions
CONS
- High setup cost
- Low accuracy in different
conditions
- Need frequent
maintenance/replacement
- Limited types of analytics
Hardware Sensors

7. BEFORE
PROS
- One time setup cost
- No training needed
- Large area coverage
CONS
- Low accuracy
- Limited types of analytics
- Setup cost is extra large
GSM/WiFi/AERIAL

8. NOW
PROS
- Low setup cost
- High accuracy in any
conditions
- Large types of analytics
- Data can be reused
CONS
- Need much data for
training
- Hard to implement
- Still not so many specialists
in this area
- Need manual work at
training stage
Machine Learning & Computer Vision

9. HOW IT LOOKS LIKE

12. NON DEEP LEARNING
TECHNIQUES

13. PREPROCESSING
Strong influence of noise and environmental changes
Outdoor conditions based filtering
Histogram and camera specs based equalization
Noise reduction

14. DETECTION & CLASSIFICATION
Using different types of descriptors ensemble &
postprocessing
/ HOG / SIFT / SURF / ORB / Shape context

15. DETECTION & CLASSIFICATION
HOG | Histogram of oriented gradients
Image braked on cells
For each cell pixel calculating
gradient
For each cell, based on gradients,
creating histogram
Bunch of cells histograms
normalized, based on
block(N cells) contrast
Info: https://goo.gl/Y3V6Xv

16. DETECTION & CLASSIFICATION
SIFT | Scale-Invariant Feature Transform
Get Gaussians & DoG(Difference of Gaussians)
pyramids based on N time scaled image.
Finding local extremums. Point is local
extremum(keypoint) if > or < all surrounding points
in image DoG and all layers of DoG Pyramid
Check extremums with approximation with
second-order Taylor polynomial
Getting orientation based on surrounding points
Creating descriptor
Info: https://goo.gl/4QaWCK

17. DETECTION & CLASSIFICATION
SURF / ORB | Speeded-Up Robust Features / Oriented FAST and Rotated BRIEF
SURF - faster SIFT. Approximate Laplacian of Gaussian with convolution and
use additional methods to speed up processing.
ORB - An open source efficient alternative to SIFT or SURF.
SURF: https://goo.gl/PTYkC9 ORB: https://goo.gl/XfXPzN

18. DETECTION
BG SUBTRACTION
Moving Objects = current_frame - background_layer
Where background_layer is a static
frame without any interested
objects and moving.
Tutorial: https://goo.gl/v1dbYS

20. So what we saw?
Using layers in DoG pyramid
Using kernel filtering in gradients &
extremums
Using convolution for speed up
Using ROI in BG subtraction
What also use all of this things?

21. CNN
Convolutional Neural
Network

22. DEEP LEARNING
TECHNIQUES

23. Deep Neural Network - Neural Network composed from
several layers.
Convolution
Matrix Kernel
1, 0, 2 1, 1, 1
0, 1, 1 X 1, 1, 1 =
0, 2, 0 1, 1, 1
= 1x1 + 0x1 + 2x1 +
0x1… = 7

24. DCNN - Deep Convolutional Neural Network.
Fully connected layers - convolutions or perceptron

25. DCNN - Deep Convolutional Neural Network.

26. MAIN TYPES OF NETWORKS
YOLOv2Faster RCNN with
Inception Resnet v2
conv
ROI projection
Conv feature map
Detection
SSD500 with Inception
v2
Nvidia GeForce GTX 960M (600 cores)
2fps 5.5fps 8fps

28. WHATS NEXT?

29. Cars with sensors
⬇
Autonomous Cars
⬇
Autonomous Traffic System

30. KAI VISION
TRAFFIC ANALYTIC SYSTEM
Making city better with accurate analytics
Andrey Nikishaev
CTO
andrey.nikishaev@gmail.com
Medium: a.nikishaev
Facebook: anikishaev
How To Become A Machine Learning Engineer: https://goo.gl/q9wS8u