detection and classification of vehicles using stereo vision
TRANSCRIPT
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DETECTION AND CLASSIFICATION OF VEHICLES USING STEREO VISION
UNIVERSITÀ DEGLI STUDI DI PARMA FACOLTÀ DI INGEGNERIA
CORSO DI LAUREA SPECIALISTICA IN INGEGNERIA INFORMATICA
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Piero Micelli
STEREO VISION USED FOR THE EXTRACTION OF 3D INFORMATION FROM HOMOLOGOUS POINTS
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COMPUTE DISPARITY MAP
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• Vehicles counting • Vehicles counting per direction • Vehicle classification
GOALS
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INPUTS• Stereo video sequences • Relative disparity map
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ALGORITHM STEPS
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OBJECT DETECTION
TRACKING
OBJECT CLASSIFICATION
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OBJECT DETECTION
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• Use of v-disparity to localize an object
V-DISPARITY OF FREE ROAD IS A STRAIGHT LINE
PATTERN OF AN OBJECT ON THE ROAD
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OBJECT DETECTION
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• ISSUE: different vehicles give different patterns
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OBJECT DETECTION• SOLUTION: create a model of the free road and
use it to use as baseline
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OBJECT DETECTION
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• SOLUTION: use the model of road to find the pattern (spotting the difference from the baseline)
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OBJECT DETECTION
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1. The v-disparity does not give information about the width, but only on the length
2. What if there are two objects in parallel?
FURTHER IUSSES
SOLUTION
• Use the u-disparity
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OBJECT DETECTION• Use of u-disparity to check the object presence
U-DISPARITY OF FREE ROAD OBJECT PATTERNU-DISPARITY IN PRESENCE OF VEHICLE
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OBJECT DETECTION
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v-disparity and u-disparity models
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CASES 1. The u-disparity shows a single object: OK 2. More object are detected on the u-disparity: ? 3. No object on u-disparity: ?
OBJECT DETECTION
Use the foreground image for a third comparison!
If an object is detected using the v-disparity, we look at the u-disparity for further information
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• Example 1: one or two object on the right lane? OBJECT DETECTION (Uncertain Case)
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• Example 1: foreground confirm two object OBJECT DETECTION (Uncertain Case)
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OBJECT DETECTION (Uncertain Case)• Example 2: one or two object on left lane?
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OBJECT DETECTION (Uncertain Case)
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• Example 2: foreground confirm one object
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OBJECT DETECTION (Uncertain Case)
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• Example 3: one or no object on left lane?
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OBJECT DETECTION (Uncertain Case)
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• Example 3: foreground confirm one object
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OBJECT DETECTION
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• Detect objects separately for each side of the road
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• Centroid Id
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OBJECT DETECTION
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CID
• Bounds • Width • Height • Length • Lane
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TRACKING
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• Tracking for each lane of the detected CIDs
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TRACKING
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• A vehicle could travel across the two-lane • A vehicle could move from one lane to another
IUSSES
SOLUTIONS• Tracking of CIDs detected on both lanes • Association of CIDs to a vehicle (VID)
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TRACKING
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When two CIDs are the same vehicle? Edge shared and same direction CIDs belongs to same vehicle
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TRACKING
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TRACKING
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TRACKING
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• VEHICLE ID
VID
• CIDs Bounds • VID Bounds • Position History • Width Histoty • Height History • Length History • Direction
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OBJECT CLASSIFICATION
FEATURES:
• W: vehicle width (u-disparity) • L: vehicle length (v-disparity) • H: vehicle height (v-disparity)
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OBJECT CLASSIFICATION
• Decision tree
VEHICLES
4 WHEELS TRUCK
4 WHEELS TRUCK
2 WHEELS
W<Sth
W>Sth1, L>Sth2, H >Sth3
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RESULTS
Analyzed video sequences:
• Number: 6 • Recorded during different seasons • Recorded during different daytime hours • Similar point of view
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RESULTS
CATEGORIA TOTALE ERRORI ERRORE (%)VEHICLES 744 0 0 %4 WHEELS 705 4 0.6 %TRUCK 13 2 15 %2 WHEELS 26 1 3 %
SUD NORDTOTALE ERRORI TOTALE ERRORI
VEHICLES 419 1 325 0
o CLASSIFICATION
o DIRECTION
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FUTURE DEVELOPMENTS
• Try to train and to use a classifier (Support Vector Machines ...)
• Try to use more features (SIFT) • Use an algorithm to detect roadsides