DETECTION AND CLASSIFICATION OF VEHICLES USING STEREO VISION

UNIVERSITÀ DEGLI STUDI DI PARMA FACOLTÀ DI INGEGNERIA

CORSO DI LAUREA SPECIALISTICA IN INGEGNERIA INFORMATICA

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

• 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