Machine vision based pedestrian detection and lane departure

1,126 views

Published on

Published in: Education, Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,126
On SlideShare
0
From Embeds
0
Number of Embeds
4
Actions
Shares
0
Downloads
70
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Machine vision based pedestrian detection and lane departure

  1. 1. Group Members:Sanket R. Borhade BE-E-16 Manthan N. Shah BE-G-65 Pravin D. Jadhav BE-E-41
  2. 2. Need Analysis • 41% of the total traffic accident casualties are due to abnormal lane changing. • More than 535 pedestrians die in road accidents every year. • Pune City has the highest rate of accidents amongst 27 other cities in the India. • Need for cost effective life saving tool. • Easy to install in any locomotive.
  3. 3. Introduction • Video-based car navigation systems are emerging as the next generation technology. • Object information is gathered via cameras and then, feature extraction is performed to obtain edge, colour, object details. • We are developing a system which comprises Pedestrian Detection System(PDS) and Lane Detection and Warning System(LDWS) for Medium-Class Cars Worldwide.
  4. 4. Overview (LDWS) Input image Selected lane and position Indicator on screen 1. A novel technique is used to recognize lane for a various road and illumination, lane markings conditions such as damaged road surfaces blocked by a car, shadow, backlights, etc. 2. The basic transform used will be HOUGH transform along with the segmentation of image concept to detect the lanes without any errors or flaws.
  5. 5. Overview
  6. 6. Overview (PDS) • To achieve high detection speed with good detection performance, a Two-step framework method was proposed.
  7. 7. System Block Diagram Input image from camera Detection of lane or pedestrian Indicating the result by means of visuals or audio
  8. 8. Camera • Color Camera Detection under normal /Day conditions • IR Camera Detection in night/ low light conditions
  9. 9. Lane Detection • Segmentation Increasing the detection Rate • Hough Transform Used for Classification
  10. 10. Pedestrian Detection • 2 Step Method
  11. 11. Pedestrian Detection Contd. • Haar like features • Triangular Feature Set • Edgelet • Shapelet
  12. 12. Results
  13. 13. Hardware Details o TMS302C6703 Highest-Performance Floating Point DSP Advanced Very Long Instruction Word (VLIW) TMS320c76x DSP Core 32-Bit External Memory Interface o CMOS Camera o IR Camera o Speed Sensor
  14. 14. Advantages • • • • Combination of both LDW and PD systems Improved speed and detection rate Low False Rate Intended for low cost applications
  15. 15. Applications • • • • Vehicle Driver Assistance Systems Automated Surveillance Military Application Security
  16. 16. Thank you

×