Recommended
More Related Content
What's hot
What's hot (20)
Recently uploaded
Recently uploaded (20)
Detection and classification of vehicles using stereo vision
- 1. DETECTION AND CLASSIFICATION OF VEHICLES USING STEREO VISION UNIVERSITÀ DEGLI STUDI DI PARMA FACOLTÀ DI INGEGNERIA CORSO DI LAUREA SPECIALISTICA IN INGEGNERIA INFORMATICA
- 2. Piero Micelli STEREO VISION USED FOR THE EXTRACTION OF 3D INFORMATION FROM HOMOLOGOUS POINTS 19/05/2014 2 COMPUTE DISPARITY MAP
- 3. Piero Micelli • Vehicles counting • Vehicles counting per direction • Vehicle classification GOALS 19/05/2014 3 INPUTS • Stereo video sequences • Relative disparity map
- 4. Piero Micelli ALGORITHM STEPS 19/05/2014 4 OBJECT DETECTION TRACKING OBJECT CLASSIFICATION
- 5. Piero Micelli OBJECT DETECTION 19/05/2014 5 • Use of v-disparity to localize an object V-DISPARITY OF FREE ROAD IS A STRAIGHT LINE PATTERN OF AN OBJECT ON THE ROAD
- 6. Piero Micelli OBJECT DETECTION 19/05/2014 6 • ISSUE: different vehicles give different patterns
- 7. Piero Micelli19/05/2014 7 OBJECT DETECTION • SOLUTION: create a model of the free road and use it to use as baseline
- 8. Piero Micelli OBJECT DETECTION 19/05/2014 8 • SOLUTION: use the model of road to find the pattern (spotting the difference from the baseline)
- 9. Piero Micelli OBJECT DETECTION 19/05/2014 9 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
- 10. Piero Micelli19/05/2014 10 OBJECT DETECTION • Use of u-disparity to check the object presence U-DISPARITY OF FREE ROAD OBJECT PATTERNU-DISPARITY IN PRESENCE OF VEHICLE
- 11. Piero Micelli OBJECT DETECTION 19/05/2014 11 v-disparity and u-disparity models
- 12. Piero Micelli19/05/2014 12 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
- 13. Piero Micelli19/05/2014 13 • Example 1: one or two object on the right lane? OBJECT DETECTION (Uncertain Case)
- 14. Piero Micelli19/05/2014 14 • Example 1: foreground confirm two object OBJECT DETECTION (Uncertain Case)
- 15. Piero Micelli19/05/2014 15 OBJECT DETECTION (Uncertain Case) • Example 2: one or two object on left lane?
- 16. Piero Micelli OBJECT DETECTION (Uncertain Case) 19/05/2014 16 • Example 2: foreground confirm one object
- 17. Piero Micelli OBJECT DETECTION (Uncertain Case) 19/05/2014 17 • Example 3: one or no object on left lane?
- 18. Piero Micelli OBJECT DETECTION (Uncertain Case) 19/05/2014 18 • Example 3: foreground confirm one object
- 19. Piero Micelli OBJECT DETECTION 19/05/2014 19 • Detect objects separately for each side of the road
- 20. • Centroid Id Piero Micelli OBJECT DETECTION 19/05/2014 20 CID • Bounds • Width • Height • Length • Lane
- 21. Piero Micelli TRACKING 19/05/2014 21 • Tracking for each lane of the detected CIDs
- 22. Piero Micelli TRACKING 19/05/2014 22 • 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)
- 23. Piero Micelli TRACKING 19/05/2014 23 When two CIDs are the same vehicle? Edge shared and same direction CIDs belongs to same vehicle
- 26. Piero Micelli TRACKING 19/05/2014 26 • VEHICLE ID VID • CIDs Bounds • VID Bounds • Position History • Width Histoty • Height History • Length History • Direction
- 27. Piero Micelli19/05/2014 27 OBJECT CLASSIFICATION FEATURES: • W: vehicle width (u-disparity) • L: vehicle length (v-disparity) • H: vehicle height (v-disparity)
- 28. Piero Micelli19/05/2014 28 OBJECT CLASSIFICATION • Decision tree VEHICLES 4 WHEELS TRUCK 4 WHEELS TRUCK 2 WHEELS W<Sth W>Sth1, L>Sth2, H >Sth3
- 29. Piero Micelli19/05/2014 29 RESULTS Analyzed video sequences: • Number: 6 • Recorded during different seasons • Recorded during different daytime hours • Similar point of view
- 30. Piero Micelli19/05/2014 30 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 NORD TOTALE ERRORI TOTALE ERRORI VEHICLES 419 1 325 0 o CLASSIFICATION o DIRECTION
- 31. Piero Micelli19/05/2014 31 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