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Autonomous Learning of Object Models on Mobile Robots

Autonomous Learning of Object Models on Mobile Robots. Xiang L i Ph.D. student supervised by Dr. Mohan Sridharan Stochastic Estimation and Autonomous Robotics Lab (SEARL) Department of Computer Science Texas Tech University October 2 3 , 2012. Object Recognition. Challenges:

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Autonomous Learning of Object Models on Mobile Robots

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  1. Autonomous Learning of Object Models on Mobile Robots Xiang Li Ph.D. student supervised by Dr. Mohan Sridharan Stochastic Estimation and Autonomous Robotics Lab (SEARL) Department of Computer Science Texas Tech University October 23, 2012

  2. Object Recognition • Challenges: • How to identify ROI in the image (Region Of Interest) ? • What features to extract in ROI (Object model) ? • Efficient Implementation. ROI match Test Learn Object Model color, texture or shape

  3. Related Work • Object model O. Bjorn, PAMI12; R. Fergus, CVPR03; P. Felzenszwalb, IJCV04; N.E.K. Roman, AR10. • Mobile robot C. Guo, ICRA11; M. Sridharan, IROS07;D. Parikh, PAMI12; J. Hoey, CVIU10. • Local image feature D. Lowe, IJCV05; J. Matas, BMVC02; S. Park, IROS09; M. Calonder, ECCV10.

  4. Motivation • Goal • Learn and recognize objects autonomously in dynamic environments. • Our Work • Identify ROIs autonomously based on a limited number of images with moving objects. • Build probabilistic object models using the complementary properties of different visual cues. • Fuse the information by generative model and energy minimization algorithm.

  5. Autonomously Learning • Supervised Learning • Images with the labeled regions • Unsupervised Learning • Images without any labeled regions • Track and cluster local image gradient features [128D vector] • A short sequence of images (motion cue) ROI ROI t t+1

  6. Object Model • Given ROI (autonomous or manual) • Use the complementary properties of different visual cues

  7. Motivation of SCV and Undirected Graph • Match by Nearest Neighbor algorithm(shortest Euclidean distance). • The difference between correct and incorrect match • The spatial arrangements of local features • The connection between the local features correct Incorrect

  8. SCV from gradient features • The individual gradient features may not be unique. • The spatial arrangement of local gradient features corresponding to a specific object is difficult to duplicate.

  9. Connection Potentials • Connection potential is computed as the color distribution of pixels between gradient features in the image ROI. • Build an undirected graph of connection potentials to model the neighborhood relationships between gradient features.

  10. Parts from image segments • Considers the arrangement of object parts made up of image segments. • Pixels within a part have similar values, while pixels in neighboring parts have dissimilar values.

  11. Color-based Representation • Computes the distance between every pair of pdfs and models the distribution of distances as a Gaussian. … … Color histogram (pdf) Second order color distribution statistics

  12. Local context from image segments • Probabilistic mixture models • Relative positions (on, under, beside)

  13. Information Fusion • Generative model • considers the relationship between the components • Energy minimization algorithm • Identifies the ROI for recognizing the stationary objects

  14. Robot Platforms: Erratic • 1.6GHz Core2 Duo CPU • 2 cameras (monocular & stereo) • Laser range finder • 640 × 480 Resolution • Wi-Fi • On board computation

  15. Experimental Trial Test Image Match Probabilities Net Match

  16. Experimental Trial (Cont) Test Image Match Probabilities Net Match

  17. Object Categories

  18. The Classification Accuracy

  19. Conclusions • Mobile robot can identify interesting objects based on motion cues, and autonomously and efficiently learn object models that exploit the complementary properties of appearance-based and contextual visual cues. • Exploiting the complementary properties of these visual cues enables the robot to use generative model and energy minimization algorithms to reliably and efficiently recognize the learned.

  20. Future Work • Consider image sequences with multiple moving objects. • Add Shape representation into object model. • Extend to a team of robots collaborating in dynamic environments.

  21. Q & A

  22. Convex Hull • The convex hull of a set of points is the smallest convex set that contains the points. • Quickhull algorithm1 computes the convex hull. 1. Barber, C.B., Dobkin, D.P., and Huhdanpaa, H.T., “The Quickhull algorithm for convex hulls”, ACM Trans. on Mathematical Software, 22(4):469-483, Dec 1996, http://www.qhull.org

  23. Gamma distribution • Context Probability • Context Probability Using Gamma

  24. Experiment Example • Learned robot model (Corridor) • Testing • Corridor(1) • Prob = 0.89 • Corridor(2) • Prob = 0.77 • Office • Prob = 0.12

  25. Generative Model • Randomly generating observable data • Typically given some hidden parameters • Specifies a joint probability distribution over observation and label sequences. 

  26. Generative model (Cont)

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