autocalib: automatic calibration of traffic cameras at...
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AutoCalib: Automatic Calibration of Traffic Cameras at Scale
Romil Bhardwajβ , Gopi Krishna Tummala*, Ganesan Ramalingamβ , Ramachandran Ramjeeβ , Prasun Sinha*
β Microsoft Research, *The Ohio State University
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450
2012 2013 2014 2015 2016
Nu
mb
er
of
Cam
era
s (M
illio
n)
Number of Security Cameras Worldwide
Source: IHS
Conventional Traffic Camera Uses
Post-facto Incident ReviewManual Surveillance
Emerging Traffic Camera Use Cases
Vehicle Speed Measurement(without dedicated sensors)
Traffic Analytics Near Miss Stats
All require distance measurements in the scene
Measuring Distances in an Image
220 px = 8 m
220 px = 34 m
Camera CalibrationReal-world Coordinates (m) <-> Image Coordinates (px)
Camera Calibration
π¦ =ππ₯ 0 ππ₯0 ππ¦ ππ¦0 0 1
π11 π12 π13 π‘1π21 π22 π23 π‘2π31 π32 π33 π‘3
π₯
Intrinsic Matrix(Focal length, camera center)
Extrinsic Matrix(Rotation, Translation)
ImageCoordinates
Real WorldCoordinates
π
π
βHardβ Calibration
π¦ =ππ₯ 0 ππ₯0 ππ¦ ππ¦0 0 1
π11 π12 π13 π‘1π21 π22 π23 π‘2π31 π32 π33 π‘3
π₯
Intrinsic Matrix(Focal length, camera center)
Extrinsic Matrix(Rotation, Translation)
ImageCoordinates
Real WorldCoordinates
Not Scalable!
βSoftβ Calibration
π¦ =ππ₯ 0 ππ₯0 ππ¦ ππ¦0 0 1
π11 π12 π13 π‘1π21 π22 π23 π‘2π31 π32 π33 π‘3
π₯
Intrinsic Matrix(Focal length, camera center)
Extrinsic Matrix(Rotation, Translation)
ImageCoordinates
Real WorldCoordinates
βEPnP Solver
βSoftβ Calibration - Prior Art
Chessboard Calibration
Vanishing Points
Geometric Landmarks
No Chessboard Patternsin Traffic Views
Assumption ofStraight Line Motion
Assumption ofLandmarks
AutoCalib Overview
AutoCalibπ
π
Traffic Video Calibration Estimate
AutoCalib: no humans-in-the-loop, robust calibration
Video FramesVehicle
DetectionKeypoint
Extraction
Calibrations Set
Vehicle Geometric Dimensions
Calibration
Geometry based filters
Calibration Values
Cropped Image Vehicle Keypoints
ππΆ
ππΆππΆ
ππΊ
ππΊ
ππΊ
π , ππΉπ, π»ππΉπ, π»π
:
AutoCalib - Pipeline
Vehicle Detection
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Vehicle Detection
β’ Off-the-shelf DNNs (Fast-RCNN, YOLO) promise state of the art accuracyβ’ Expensive, scene often empty
β’ Background Subtraction is fastβ’ Inaccurate
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Solution - Trigger the DNN with Background Subtraction
Key-point Extraction
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Key-point Selection
Desired Properties
1. Visually Distinct
β’ Ease of detection
2. Non-planar
β’ Robust Calibrations
vs
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Key-point Extraction
β’ Statistical vision based techniques arenβt robust to lighting variations
β’ DNNs require a lot of labelled dataβ’ No datasets available
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Transfer learn a DNN on a smaller dataset
Transfer Learning - Primer
Convolution and Pooling Layers(Generic Features)
Fully Connected Layers(Car Model Classification)
Output:BMW 3 Series
Transfer Learning - Primer
Convolution and Pooling Layers(Generic Features)
Fully Connected Layers(now detecting key-points)
Output:Key-points (x,y)
Transfer Learning - Less Data, Faster Training
Key-point DNN Dataset
β’ Manually labelled key-points on 486 car images
β’ Image Augmentation
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Original Img Horz Mirror Horz Mirror Rotate
Horz Mirror Crop
Original Crop
Original Rotate
Total of 10,344 images post augmentation
Key-point DNN Trainingβ’ GoogLeNet architecture trained on CUHK CompCars dataset (CVPR β15)
for Car make/model classification
β’ Replaced last two fully connected layers with keypoint regression outputs
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Key-point DNN Performance
~80% of Key-points < 10% error
Calibration Estimation
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
π¦ =ππ₯ 0 ππ₯0 ππ¦ ππ¦0 0 1
π11 π12 π13 π‘1π21 π22 π23 π‘2π31 π32 π33 π‘3
π₯
Intrinsic Matrix(Focal length, camera center)
Extrinsic Matrix(Rotation, Translation)
ImageCoordinates
Real WorldCoordinates
Vehicle Identification at low resolutionβ¦
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
β¦ is hard!(for both, humans and machines)
Canβt identifyβ¦ so, approximate!
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
R1, T1
R3, T3
R2, T2
Calibrate
n Modelsn Calibrations
(Toyota Prius, Toyota Corolla, Honda Civic, Volkswagen Jetta, BMW 320i, Audi A4, etc.)
Calibrate with most popular cars
Errors in Calibration
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Key-point Prediction ErrorsModel Approximation Errors
Statistical filters to remove outliers and average
Key Insight 1
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Ground plane should be consistent across all Calibrations
The Orientation Filter
1. For calibration π π , ππ , its Z-axis orientation Τ¦π§
is defined by vector π β,3π
2. Let Τ¦π§ππ£π = π΄π£πππππ(π β,3π )
3. Pick π% calibrations with the least deviation
between Τ¦π§ and Τ¦π§ππ£π
π 1, π1
π 2, π2
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Key Insight 2
Distance to a fixed point must be consistent across Calibrations
π
π
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
The Displacement Filter
β’ Focus region: Region where cars are detected
β’ For each Calibration:
1. Point ππ = projection of center of focus region on the ground plane
using (π π , ππ)
2. ππ = Distance of ππ to camera
β’ Pick middle π% and filter the rest
ππ
ππ
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Filtering Overview
Orientation Filter (75%)
Displacement Filter (50%)
Average Rotation Matrix
Orientation Filter (75%)
(π πππππ , ππππππ)
(π 1, π1) (π 2, π2) (π 3, π3)
β¦ . .
Displacement Filter (Pick median)
(π ππ£π, π1) (π ππ£π, π
2) β¦ . .
Video Frames Vehicle DetectionKeypoint
ExtractionCalibrations SetCalibration
Geometry based filters
Calibration Values
Implementation
Azure Service β 4 Tesla K80s, 224 GB RAM
< 12% error with ~8 minutes of video
Evaluation - Dataset
β’ 350+ hours from 10 traffic cameras in
Seattle
β’ Resolution - 640x360 to 1280x720
β’ Ground truth distances and calibration
estimated using Google Earth
A
B D
EF G
Camera Image
A
B D
EF G
8m
8m
12m9m
Google Earth View
Evaluation
AutoCalib vs Manual Calibration
4.8 5.3 5.1 5.5
8.2
1.83.0
5.1
1.5
5.9
9.8
12.3
7.9
10.6 11.1
6.7
10.211.1
5.1 5.1
0
4
8
12
16
20
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
RM
S E
rror
(%)
Ground Distance Measurement, RMS Error (%)
Manual Calibration AutoCalib EstimateAutoCalib achieves <12% RMS error in measuring distances
AutoCalib vs Prior Art
9.812.3
7.910.6 11.1
6.7 10.2 11.1
5.15.1
16.8 14.920.3
28.8
15.8
5.4
23.019.4
14.7
56.8
0
10
20
30
40
50
60
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
RM
S E
rro
r (%
)
Ground Distance Measurement, RMS Error (%)
AutoCalib Calibration VP Approach [1]
[1] DubskΓ‘ et al., Fully automatic Roadside Camera Calibration for Traffic Surveillance. IEEE ITS 2015
AutoCalib outperforms prior state of the art approaches
Does more video data help?
AutoCalib converges with increasing vehicle detections
Application β Speed Measurement
AutoCalib Summary
β’ Camera Calibration
β’ Enables distance measurements
β’ Highly manual today
β’ AutoCalib
β’ Scalable automatic calibration
β’ Uses DNNs to analyze vehicle geometry
β’ Experiments
β’ < 12% error in measuring distances
β’ Calibrates with few hundred detections
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