Probabilistic Model of Object Detection Based on Convolutional Neural Network

The combination of a CNN detector and a search framework forms the basis for local object/pattern detection. To handle the waste of regional information and the defective compromise between efficiency and accuracy, this paper proposes a probabilistic model with a powerful search framework. By mapping an image into a probabilistic distribution of objects, this new model gives more informative outputs with less computation. The setting and analytic traits are elaborated in this paper, followed by a series of experiments carried out on FDDB, which show that the proposed model is sound, efficient and analytic.

[1]  Koen E. A. van de Sande,et al.  Segmentation as selective search for object recognition , 2011, 2011 International Conference on Computer Vision.

[2]  Yann LeCun,et al.  Transformation Invariance in Pattern Recognition - Tangent Distance and Tangent Propagation , 2012, Neural Networks: Tricks of the Trade.

[3]  Bernard Victorri,et al.  Transformation invariance in pattern recognition: Tangent distance and propagation , 2000 .

[4]  Erik Learned-Miller,et al.  FDDB: A benchmark for face detection in unconstrained settings , 2010 .

[5]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[6]  Xiaogang Wang,et al.  DeepID-Net: Deformable deep convolutional neural networks for object detection , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Xiaogang Wang,et al.  DeepID-Net: multi-stage and deformable deep convolutional neural networks for object detection , 2014, ArXiv.

[8]  Gang Hua,et al.  Probabilistic Elastic Part Model for Unsupervised Face Detector Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.

[9]  Gang Hua,et al.  A convolutional neural network cascade for face detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Erik G. Learned-Miller,et al.  Online domain adaptation of a pre-trained cascade of classifiers , 2011, CVPR 2011.

[11]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[12]  Sudeep D. Thepade,et al.  A Comparison of Kekre's Fast Search and Exhaustive Search for Various Grid Sizes Used for Colouring a Greyscale Image , 2010, 2010 International Conference on Signal Acquisition and Processing.

[13]  Rogério Schmidt Feris,et al.  A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection , 2016, ECCV.