Vehicle Tracking using Optical Features

Transcription

1 Vehicle Tracking using Optical Features Songlin Piao Robotics Research Lab University of Kaiserslautern, Germany

2 Outline Motivation State-of-Art Proposed System Experiments Conclusion

3 Motivation Autonomous Universal Autonomous Car will benefit from this algorithm. The algorithm can be adapted to other types of rigid object tracking easily. (e.g. Face Tracking)

4 State-of-Art I Long-term Tracking Methods TLD (Tracking-Learning-Detection ) Drawbacks: 1. Not able to estimate the rotation of object. 2. Inaccurate scaling estimation. 3. Unable to locate the object when the object out of the image partially CMT (Consensus-based Matching and Tracking of Keypoints for Object Tracking ) Drawbacks: 1. The original object model will not be updated. 2. Inaccurate matching with BRISK descriptor when new appearance of object appears.

5 Vehicle Tracking System Structure Detector Tracker Dynamic Update Module Tracking Rotation, Scaling Estimation Detection Model Update

6 Contribution C-BRISK descriptor C-BRISK is based on BRISK keypoints detection method. C-BRISK adds color information in binary BRISK descriptor to improve the performance of BRISK. Dynamic update module for object Dynamically updates object model to instead of the static object model. Comparison between various kinds of descriptors SIFT, SURF, ORB, BRISK, C-BRISK Applying various detection methods and corresponding descriptors in this tracking system and comparing the performance of detector, tracker and updating module with these descriptors.

7 Matching Based Detection I Detector

8 Matching Based Detection II Detector flow chart Keypoints Detection

9 BRISK Keypoints Detection FAST keypoints detection BRISK keypoints detection -BRIEF,ORB -Threshold are needed to control the number of keypoints produced. -Combined the advantages of SIFT,SURF and ORB detection methods.

10 Matching Based Detection Detector flow chart Generating descriptors

11 BRISK Descriptor The local gradient of keypoint pair (Pi, Pj ) can be represented as g : Each bit of the binary BRISK descriptor can be determined as following:

12 C-BRISK Descriptor For color image: 3 by 3 pattern m(r,g,b): Median value of R,G,B value For gray image: C-BRISK change the m(r,g,b) to the median value of gray values of the 3 by 3 pattern and keep the length of C-BRISK at 536 bits.

13 BRISK VS C-BRISK BRISK C-BRISK

14 Matching Based Detection Detector flow chart Object model initialization n-1 n 0

15 Tracking Module Tracker

16 Tracking Module L-K Tracking Method & Forward-Backward Error Method - d is the image velocity of point x - u is the previous position of x - v is the current position of x Forward-Backward Error Method

17 Dynamic Updating Module I Dynamic Updating Module

18 Dynamic Updating Module II Dynamic Updating Module VS Learning Module in TLD Similarity: Updating of both methods are based on the tracking result. Dynamic updating module structure Difference: TLD updates the the training data (patchs) for cascade classifiers while DUM updates the descriptors of keypoints in the object model for better matching of keypoints belongs to object.

19 Dynamic Updating Module III Object Model Update Model

20 Experiments I Keypoints detection test SIFT SURF BRISK ORB

21 Experiments I Number of Keypoint and time

22 Experiments II Recall Precision C-BRISK C-BRISK BRISK BRISK ORB ORB SURF SURF SIFT SIFT

23 Experiments III Update Update

24 Experiments III

25 Experiments IV C-BRISK method with rotation

26 Conclusion and Outlook -C-BRISK is more accurate to describe the keypoints than BRISK. -Tracker improves the performance of system for most descriptors. -Comparing with the other descriptors in the experiments proves that C-BRISK and dynamic updating module improve performance of this system. -C-BRISK also can be improved by optimizing weightings of color information in this descriptor. -The timing of updating the descriptor could be a dynamic processing according to different situations. -The dynamic updating module used in this thesis could be improved by updating the background model.

27 Thank you! Questions?