1. DETECTION AND CLASSIFICATION OF VEHICLES FROM A VIDEO USING TIME-SPATIAL IMAGE NAFI UR RASHID, NILUTHPOL CHOWDHURY, BHADHAN ROY JOY S. M. MAHBUBUR RAHMAN Department of Electrical and Electronic Engineering Bangladesh University of Engineering And Technology Dhaka – 1000, Bangladesh ICECE 2010

2. WHY VEHICLE DETECTION AND CLASSIFICATION SYSTEM (VDCS)? Traffic flow parameter extraction Intelligent transportation system Automated traffic control Automated vehicle counting Automated checking of toll collection Toll booth – Bridges, Avenues Parking lot – Hospital, Shopping Mall Detection of traffic violation Speed monitoring Lane monitoring ICECE 2010

3. COMMON TECHNIQUES Mechanical techniques - Induction Loop Sensor Pneumatic Road Tube Weight-in-motion Sensor Piezoelectric Cable Sensor ICECE 2010

4. LIMITATIONS ICECE 2010 High space requirement High installment & maintenance cost Setup & repair process time consuming Calibration Mechanical Error Hardware Based

5. ICECE 2010 SMART APPROACH - VIDEO PROCESSING Nonintrusive method. Less installation and maintenance cost. No disruption of traffic for installation and repair. Remote traffic surveillance Efficient classification of vehicles Software based Features & parameters are adaptive Advanced DSP algorithms could be applied

6. EXISTING VIDEO-BASED DETECTION SYSTEM Motion-based systems Optical Flow Gaussian Model Background Subtraction ICECE 2010 Problems of existing systems: Heavy computational load Highly sensitive to jittering & pixel intensity Less suitable for real-time implementation

7. PROPOSED METHOD VIRTUAL DETECTION LINE VIRTUAL DETECTION LINE BASED METHOD Time Spatial Image (TSI) Generation contains both temporal and spatial information Vehicular width can be approximated Ensures faster extraction of Key Vehicular Frame (KVF)Key Vehicular Frame Tracking independent Only one frame per classification Simple yet efficient Low computational load ICECE 2010

8. VIRTUAL DETECTION LINE Virtual Detection Line ICECE 2010 A strip of pixel perpendicular to the direction of vehicle travelling Back Frame 1 Frame 2

9. TSI GENERATION ICECE 2010 Frame 12

10. TIME SPATIAL IMAGE (TSI) ICECE 2010 Frame 692

11. EDGE DETECTOR EDGE DETECTION ICECE 2010

12. MORPHOLOGICAL OPERATIONS EDGE DETECTOR MORPHOLOGICAL OP. ICECE 2010

13. Bounding Box Center of Bounding Box TSI PROCESSING TSI Vehicular Blob (TVB) Width Source video

14. ICECE 2010 TSI PROCESSING Center of Bounding Box

15. KEY VEHICULAR FRAME A time frame on which the midpoint of the vehicle is approximately on the VDL Only KVF requires further processing No background processing required Back Car 1 Leguna Bike Car 2 ICECE 2010

16. SEGMENTATION KVF TSI

17. ICECE 2010 MORPHOLOGICAL OP. Canny Edge Detection Blob = ((Im ⊕ Obj)ΘObj) Obj = 5x5 rectangle Filling ‘holes ’

18. FEATURE EXTRACTION ICECE 2010  Shape-based feature  Extracted from vehicle blob of TSI & KVF Feature Selection Criteria:  Distinctiveness  Computational efficiency  Sensitivity to environment  Non-Redundancy

19. FEATURES Selected Shape-Based Features:  TVB Width  Length-Width Ratio  Major Axis-Minor Axis Ratio  Area  Compactness  Solidity. ICECE 2010

20. TVB Width: Vertical length of the segmented region of TSI Vehicle Blob FEATURES ICECE 2010

21. Length-Width Ratio :. ICECE 2010 FEATURES

22. Major Axis-Minor Axis Ratio: This ellipse has the same normalized second central moments as the segmented region.. ICECE 2010 FEATURES

23. Area: Number of white pixels in the segmented region.. ICECE 2010 FEATURES

24. Compactness: Determines how compact(circular) a shape is.. ICECE 2010 FEATURES

25. Solidity: Convex Area is the area of smallest polygon that contain the region. ICECE 2010 FEATURES

26. ICECE 2010 FEATURE VECTOR TABLE

27. CLASSIFICATION K- Nearest Neighborhood (KNN)  Linear  Weighted Distance Measurement  Majority Voting Why KNN?  Sufficiently low computational complexity  Standard & optimum  Significantly good classification performance. ICECE 2010

28. CLASSIFICATION Steps of obtaining Training Data Set:  Feature vectors were obtained from handpicked vehicles  Obtained feature vectors were partitioned with Fuzzy C- Means Clustering algorithm Why FCM?  Reduction of memory requirement  Speeding up of searching time Majority voting among the training data set determines vehicle class ICECE 2010

29. EXPERIMENTAL SETUP Camera Elevation: 21 feet Camera Angle: 45 degrees Frame Rate: 25 fps Resolution: 144x176 pixels Color Profile: Monochrome Weather Condition: Sunny, Cloudy, Normal System Specification: Intel Pentium D 2.66 GHz, 1GB DDR2 RAM

30. ICECE 2010 BLOCK DIAGRAM Video- Input Extracted Frames KVF Extraction Feature Extraction Blob Detection Object Detection KNN Class Type Training Dataset TSI Generation VDLVDL

31. ICECE 2010 EXPERIMENTAL DATA Method [1]: ICPR 2002, IICETC 2009 Method [10]: In. J. Intel. Eng. Sys. 2009 Total Frames: 3082 (Sequence 1) Method [1]: 35.4s Proposed Method: 10.3s

32. FUTURE WORK  Introduction of texture based & motion invariant features to reduce classification errors  Multiple VDL Speed Calculation Improved detection & classification Occlusion minimization ICECE 2010

33. CONCLUSION  Significant improvement in terms computational load  Efficient designing of intelligent transportation system  Significantly low misclassification & misdetection rate than that of traditional methods  Practically implementable in many important sectors ICECE 2010

34. THANK YOU………