Finding objects at indoor environment combined with depth information

To detect and localize objects in a scene is an essential step for many computer vision tasks. Many efforts have been done for detecting and localizing category-specific ojects. However, only few works focused on the generic objectness measure which is more common and important than category-specific object detection. Base on an existing method, in this paper, a novel method by combining a new cue, the depth information, is proposed for detecting and localizing generic objects in the indoor scenes. Through our experiments, we found that by adding depth information cue the performance of detecting and localizing will be better (especially for the closed objects) than the original method. Finally, a method for selecting bounding box which contains a possible object is also introduced and the result is promising by being shown in our experiments.

[1]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Larry S. Davis,et al.  Real-time foreground-background segmentation using codebook model , 2005, Real Time Imaging.

[3]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Hironobu Fujiyoshi,et al.  Moving target classification and tracking from real-time video , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[5]  Akira Kawanaka,et al.  Depth estimation from stereo images using sparsity , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[6]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[7]  Thierry Bouwmans,et al.  Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey , 2008 .

[8]  Gabriela Csurka,et al.  Visual categorization with bags of keypoints , 2002, eccv 2004.

[9]  Liu Xin-ming Eye detection in color face image based on skin and Harr feature , 2008 .

[10]  Christoph H. Lampert,et al.  Beyond sliding windows: Object localization by efficient subwindow search , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Simone Milani,et al.  A Depth Image Coder Based on Progressive Silhouettes , 2010, IEEE Signal Processing Letters.

[12]  Gang Wang,et al.  Joint learning of visual attributes, object classes and visual saliency , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[13]  Pei-Jun Lee,et al.  Adaptive edge-oriented depth image smoothing approach for depth image based rendering , 2010, 2010 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB).

[14]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[16]  Pietro Perona,et al.  Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[17]  Y F Li,et al.  Determination of Stripe Edge Blurring for Depth Sensing , 2011, IEEE Sensors Journal.

[18]  Thomas Deselaers,et al.  What is an object? , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  W. Eric L. Grimson,et al.  Adaptive background mixture models for real-time tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[20]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[21]  Thomas Deselaers,et al.  Localizing Objects While Learning Their Appearance , 2010, ECCV.

[22]  Qi Tian,et al.  Foreground object detection from videos containing complex background , 2003, MULTIMEDIA '03.