Lane and Vehicle Detection
description
Transcript of Lane and Vehicle Detection
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LANE AND VEHICLE DETECTIONA Synergistic Approach
Wenxin Peng
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Structure
Lane and vehicle detection, localization and tracking
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• Reduce false positive results
• Provide more information
Structure
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Lane Detection
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IPM – Inverse Perspective Mapping
𝑥𝑔𝑟𝑜𝑢𝑛𝑑=𝐻 𝑥 𝑖𝑚𝑎𝑔𝑒
World to camera transformation
Z-direction Normalization
Parallel projection
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X
Y Z
X’
Z’
Y’
P
eye
P
PN
IPM – Inverse Perspective Mapping
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IPM – Inverse Perspective Mapping
X
Y Z
World space
Camera space
X’
Z’
Y’
ptAt
ptEye
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Lane: http://www.youtube.com/watch?v=v3mbr-qHBKI&NR=1&feature=endscreen
𝑥𝑔𝑟𝑜𝑢𝑛𝑑=𝐻 𝑥 𝑖𝑚𝑎𝑔𝑒
IPM – Inverse Perspective Mapping
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Steerable Filter
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Steerable Filter
Gaussian:
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RANSAC-Random sample consensus
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RANSAC-Random sample consensus
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Kalman Filter
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Kalman Filter
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Car Detection
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Car Detection
• Active learning
• Particle Filter
Sequencial Monte Carlo
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Tracking
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Results
Typical performance of integrated lane and vehicle tracking on highwaywith dense traffic. Tracked vehicles in the ego-lane are marked green. Tothe left of the ego-lane, tracked vehicles are marked blue. To the right of theego-lane, tracked vehicles are marked red. Note the curvature estimation.
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Results
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http://www.youtube.com/watch?v=ipXQFcAeovk&feature=endscreen&NR=1
http://www.youtube.com/watch?v=JmxDIuCIIcg&feature=endscreen&NR=1
http://www.youtube.com/watch?v=i6roGUznJ4A
Results
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Reference
• ‘Integrated Lane and Vehicle Detection, Localization, and Tracking: A Synergistic Approach’ Sayanan Sivaraman, Student Member, IEEE, and Mohan Manubhai Trivedi, Fellow, IEEE
• ‘Perspective and its Projection Transformation’, He Yuanjun (Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200030,China)
• 3-D Study Notes, Guohua Lin, 2012/7/11
• Wikipedia.com
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Thank You!