1. Vehicle Movement Tracking
Written by Goldberg Stanislav
for Vision Topics Seminar

2. Present Day Traffic Management
Magnetic Loop Detectors
Video Monitoring Systems

3. Magnetic Loop Detectors

4. Magnetic Loop Detectors
Pros:
Accurate counting
Stable under different lighting and traffic conditions
Cons:
Costly : require digging up the road surface
Unable to provide additional traffic parameters

5. Video Monitoring Systems

6. Video Monitoring Systems
Pros:
Vehicle counts and speeds
Vehicle classification: { Bus, Truck, Bike, Car }
Lane changes
Acceleration/Deceleration
Queue length for traffic jams
Less costly to install then magnetic loop detectors
Cons:
Problems with congestion ( vehicles occlusion )
Long shadows linking vehicles together
Transition between day and night

7. Tracking Requirements
Automatic segmentation of a vehicle from a background and other vehicles so there can be a unique track associated with each vehicle
Deal with variety of vehicles – motorcycles, passenger cars, buses, construction equipment, trucks, etc.
Deal with a range of traffic conditions – light midday traffic, rush-hour congestion, varying speeds in different lanes.
Deal with variety of lighting conditions – day, evening, night , sunny, overcast, rainy days.
Real-time operation of the system

8. Vehicle Tracking Approaches
3D Model based
Region based
Active contour based
Feature based

9. 3D Model based tracking
Image view is aligned with a detailed 3D model of each vehicle
Pros:
Easy vehicle classification { Bus, Truck, Bike ..}
Cons:
Memory and processing consuming approach
Unrealistic to expect to be able to have detailed models for all vehicles that are found on the roadway

10. Region based tracking Pros:
Every connected region in the image – “a blob” associated with each vehicle and then tracked over time using cross correlation. The “blobs” are found by the means of background extraction .
Pros:
Works well in free flowing traffic conditions
Cons:
Partial occlusion under congested traffic conditions leads to grouping of several vehicles into one “blob”

11. Active contour based tracking
Representing vehicle by bounding contour of the object and dynamically update it during the tracking
Pros:
Reduced computational complexity compared to region based approach
Cons:
Partial occlusion is still a problem.

12. Feature based tracking
Tracking not a object as a whole, but sub-features such as distinguished points or lines on the object.
Pros:
Partial occlusion is not a problem: some of the sub futures remains visible
Cons:
Additional problem to solve : which set of sub features belong to one object (grouping)

13. Motion Based Grouping
Based on a common motion constraint aka “common fate” : sub-features that are moving rigidly are grouped together into a single vehicle.
The grouping must be sensitive enough to pick up even slight acceleration or lane drift to distinguish a vehicle from the neighbors
Spatial proximity (sub-features spatially close one to another ) must be taken into consideration

14. Motion Based Grouping: Why it’s good for vehicle tracking?
For congested traffic vehicles are constantly changing their velocities to adjust to near by traffic, thus giving the grouper the information it needs to perform the segmentation.
For free flowing traffic, vehicles more likely to maintain constant speed with almost no lane drift, making “common fate” grouping less useful, but there is more space between vehicles.

15. The Algorithm ( David Beymer et al )
Off-line camera definition
Features detection
Features tracking
Features grouping
Obtaining traffic parameters
Vehicle Classification

16. The Algorithm

17. Off-line camera definitions (1) Line correspondence for the homography
A projective transform H , or homography, is used to map from image coordinates (x,y) to world coordinates (X,Y)

18. Off-line camera definitions (1) Line correspondence for the homography
H – linear transformation { rotation, scaling, shear, reflection }
(x,y) rotated clockwise by theta angle and translated by (x0,y0), no scaling

19. Off-line camera definitions(2) Detection regions
Stop Detection Area
Start Detection Area

20. Off-line camera definitions (3) Fiducial points for camera stabilization

21. Features detection Corner features are chosen as sub-features
Corner detector is based on image gradient

22. Features detection
Horizontal ( x axis) differentiation can be approximated by
Vertical ( y axis )
© stolen from Hagit’s Image Processing slides

23. Features detection

24. Features tracking: Kalman Filter Predictor
The position and velocity of the vehicle is described by the linear state space:
We assume that between the (k − 1)th and kth timestep the vehicle undergoes a constant acceleration of ak that is normally distributed, with mean 0 and standard deviation σa. From Newton laws of motion we conclude that:

25. Features tracking
Kalman filtering predicts the area where to search for each corner feature
Corner feature is found
The distance between predicted and measured feature is computed
If the distance is above threshold the track is rejected

26. Features tracking

27. Grouping The central principle: common motion
When sub-feature is detected , it initially connected to all neighboring tracks within certain radius
For all joined pairs of tracks pa(t), pb(t), relative displacement d(t)= pa(t)- pb(t) is calculated
For each frame d value is computed for each edge and edge is broken if either
maxtdx(t)- mintdx(t)> thresholdx
maxtdy(t)- mintdy(t)> thresholdy
Shadow sub-features tend to be unstable over time, so grouping eliminates them.

28. Grouping

29. Grouping : problems Oversegmentation
one vehicle is grouped into several segments
Overgrouping
several vehicles are grouped into one segment

30. Real Time: hardware

31. Real Time: hardware PC – Intel Pentium 150 Mhz 160 MFLOPS, 260 MIPS
C40 – TMS320C40, Texas Instruments Floating-Point Digital Signal Processor
40 MFLOPS, 20 MIPS
Present PC - Intel Core i7-920 Quad Core Processor
63000 MFLOPS, MIPS

32. Results
Length in min:sec G – number of reported vehicles (by algorithm ) N – number of actual vehicles (counted by human)

33. Results
True match: A one to one match between ground truth and a group
False negative: An unmatched ground truth
Oversegmentation: A ground truth that is matched by more then one group
False positive: An unmatched group
Overgrouping: A group that matches more then one ground truth

34. Results
Start
End

35. Results: flow scatter plot

36. Results: velocity scatter plot

37. The end