Practical 3-D Object Detection Using Category and Instance-Level Appearance Models
Title | Practical 3-D Object Detection Using Category and Instance-Level Appearance Models |
Publication Type | Conference Paper |
Year of Publication | 2011 |
Authors | Saenko, K., Karayev S., Jia Y., Shyr A., Janoch A., Long J., Fritz M., & Darrell T. |
Page(s) | 793-800 |
Other Numbers | 3210 |
Abstract | Effective robotic interaction with household objectsrequires the ability to recognize both object instances andobject categories. The former are often characterized by locallydiscriminative texture cues (e.g., instances with prominentbrand names and logos), and the latter by salient globalshape properties (plates, bowls, pots). We describe experimentswith both types of cues, combining a template-and-deformablepartsdetector to capture overall shape properties with alocal feature Naive-Bayes nearest neighbor model to capturelocal texture properties. We base our implementation on therecently introduced Kinect sensor, which provides reliable depthestimates of indoor scenes. Depth cues provide segmentationand size constraints to our method. Depth affinity is used tomodify the appearance term in a segmentation-based proposalstep, and size priors are imposed on object classes to prunefalse positives. We address the complexity of scanning windowHOG search using multi-class pruning schemes, first applyinga generic object detection scheme to prune unlikely windows,and then focusing only on the most likely class per remainingwindow. Our method is able to handle relatively cluttered scenesinvolving multiple objects with varying levels of surface texture,and can efficiently employ multi-class scanning window search. |
URL | http://www.icsi.berkeley.edu/pubs/vision/practical3dobject11.pdf |
Bibliographic Notes | Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Algarve, Portugal, pp. 793-800 |
Abbreviated Authors | K. Saenko, S. Karayev, Y. Jia, A. Shyr, A. Janoch, J. Long, M. Fritz, and T. Darrell |
ICSI Research Group | Vision |
ICSI Publication Type | Article in conference proceedings |