Transcript Structured_Hough_Voting
GM-Carnegie Mellon Autonomous Driving CRL
Structured Hough Voting for Vision based Highway Border Detection
Zhiding Yu Carnegie Mellon University 1
GM-Carnegie Mellon Autonomous Driving CRL
Autonomous Driving: Not If, But When
2
GM-Carnegie Mellon Autonomous Driving CRL
GM-CMU Collaborative Research
GM-Carnegie Mellon Autonomous Driving CRL
Sensors Setup on SRX Platform
Images from: Junqing Wei et al., “Towards a Viable Autonomous Driving Research Platform,” IEEE Intelligent Vehicles Symposium (IV), 2013
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Sensors: Price vs Information Camera Lidar Radar Price
GM-Carnegie Mellon Autonomous Driving CRL
Computer Vision Applications
Object detection
(pedestrian, vehicle, bicycle…)
Road parsing
(lane/border detection, road segmentation, vanishing point estimation…)
Localization and tracking Driver status monitoring Many other applications……
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Motivation, Description and Goal Goal
– Development for future driving assistance system and autonomous driving system – Robust detection within 0.5 to 6 meters detection range. Achieve near 100% accuracy in daytime and over 90% in nighttime on the right most lane – Handling various scenarios including highway entrance and exit – Extend to the joint system with front view 7
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High-Level Idea: Learning based Method Concrete Barrier Densely Fired scanning windows Returned Voting Points Guard Rail Soft Shoulder Concrete Barrier Guard Rail Soft Shoulder Lane Marking Structured Hough Voting Border / lane marking hypotheses
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Dataset Collection Overall 1592 training images:
1. Concrete Barrier (839 images) 2. Guard Rail (300 images) 3. Soft Shoulder (453 images)
Overall 2638 testing images:
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Training Patch Alignment Positive Samples: Negative Samples: Concrete Natural Steel Lane Marker
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Feature Extraction HOG Filter Bank Concatenated Filter Bank Feature Patches that are discriminative to HOG Patches that are discriminative to filter banks Concatenated HOG Feature
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Classification & Detection
Extract features from all training patches (based on previous page) Perform Fisher discriminant analysis Train an RBF kernel SVM Scanning window detection (Deliberately having a lot of positive firing) Concrete Barrier Guard Rail Soft Shoulder Lane Marking
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Hough Voting
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Structured Hough Voting: Intuitions
Basic philosophy: A model that assumes voting results are correlated rather than independent Inter-frame structural info on hypotheses (Temporal smoothness) Intra-frame structural info (Geometric relationship)
Multiple candidate hypotheses generation (Proposals with diversity)
1. Constrained Hough Voting on detected voting points (
Detection + Tracking
) 2. Arbitrary Hough Voting on detected voting points (
Detection
) 3. Constrained Hough Voting on image gradients (
Pure Tracking
)
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Purpose of Candidate 1
Deals most of the frames where hypotheses from consecutive frames have strong correlation.
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Purpose of Candidate 2
Automatically corrects result through searching for “much better” voting configurations (This is the power of detection, avoids error from tracking)
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Purpose of Candidate 3
In the worst case where Type 1 voters fail, perform tracking by gradients from previous pose configuration.
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Modeling under CRF: Background
A Conditional Random Field (CRF) discriminatively defines the joint posterior probability as the product of a set of
potentials Unary Potential Pairwise Potential H 1 H 2 … H N X 1 X 2 X N
The potentials are functions with hypotheses
H i
in such a way that a
larger potential value
being the variables. They are modeled generally indicates a
better hypothesis
configuration.
CRF inference seeks to find the joint hypothesis configuration
H
that maximizes
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Modeling under CRF: Intuition
What are the hypothesis H
i
?
E.g.: image pixel labels (FG/BG, Object Class, etc.), if it is a segmentation problem.
In our problem, H
i
is the Hough Voting hypothesis: H
i
= (r,
θ
).
X is the observation of voting point coordinates and their weights.
The unary potential corresponds to the exponential of Hough voting weights: exp(v(H
i
)).
The pairwise potential corresponds to the inter-frame smoothness (tracking) constraint.
H 1 H 2 … H N X 1 X 2 X N
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No Structural Information … H bd,1 H bd,2 H bd,N X 1 X 2 X
N
H ln,1 H ln,2 … H ln,N X 1 X 2 X
N
Simplest Case:
frame-wise independent Hough voting
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Adding Inter-frame Structural Info.
… H bd,1 H bd,2 H bd,N X 1 X 2 X
N
H ln,1 H ln,2 … H ln,N X 1 X 2 X
N
Adding temporal smoothness:
Hough voting constrained by neighboring frames
GM-Carnegie Mellon Autonomous Driving CRL
Adding Intra-frame Structural Info.
… H bd,1 H bd,2 H bd,N X 1 X 2 X
N
H ln,1 H ln,2 … H ln,N X 1 X 2 X
N
Adding Geometric Constraint:
Hough voting constrained by both neighboring frames and intra-frame hypotheses
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The Structured Hough Voting Model Candidate Hypotheses Generation Unit
• • •
Coupled Structure Potential
• • •
Mode Selection Potential
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The Structured Hough Voting Model
GM-Carnegie Mellon Autonomous Driving CRL
Candidate Hypotheses Generation Unit
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Mode Selection Potential
Use
decision tree
to guide the mode selection.
The mode selection basically forces the output to be one of the candidate hypotheses, but allows discrepancy with the decision tree prediction with a penalty.
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Coupled Structure Potential
The coupled structure potential captures two most important relations between a border hypothesis and a lane hypothesis
Parallelism Distance
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Inference
Conducting a whole inference each time given a new frame is computationally infeasible.
Relaxation: Initialize with the inferred state variable configuration of the previous
t
-1 frames and infer the current state variables, updating in an incremental way.
Inference procedure at t = 1:
1. Perform Hough voting for both border and lane marking 2. Perturbate hypotheses if geometric relationship violated (optional)
Inference procedure at t > 1:
1. Generate the 3 candidate hypotheses for both border and lane marking 2. Use decision tree to help selecting the best candidate 3. Perturbate candidate hypotheses if geometric relationship violated (optional) 4. Re-select the best candidate
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Experiments: Adding Coupled Structure
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Experiments: Qualitative Results Ground Truth and Baseline methods:
1. Ground Truth 2. Independent Hough voting in each frame using the fired detector voting points 3. Hough voting using the triggered detector voting points constrained by previous frame 4. Adding gradient tracking to Baseline 2.
5. Kalman filter.
6. Proposed Method
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Experiments: Quantitative Results
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Highway Entrance Detection and Lane State Tracking
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Summary
Proposed the Structured Hough Voting Model
The proposed model can be theoretically formulated under a CRF
Fast real-time feature extraction and online inference
Achieves very robust and good performance under challenging scenarios and low quality inputs from production camera
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Thank You!
Q & A