Pedestrian Detection in Infrared Images Using Fast RCNN

Compared to visible spectrum image the infrared image is much clearer in poor lighting conditions. Infrared imaging devices are capable to operate even without the availability of visible light, acquires clear images of objects which are helpful in efficient classification and detection. For image object classification and detection, CNN which belongs to the class of feed-forward ANN, has been successfully used. Fast RCNN combines advantages of modern CNN detectors i.e. RCNN and SPPnet to classify object proposals more efficiently, resulting in better and faster detection. To further improve the detection rate and speed of Fast RCNN, two modifications are proposed in this paper. One for accuracy in which an extra convolutional layer is added to the network and named it as Fast RCNN type 2, the other for speed in which the input channel is reduced from three channel input to one and named as Fast RCNN type 3.Fast RCNN type 1 has better detection rate than RCNN and compare to Fast RCNN, Fast RCNN type 2 has better detection rate while Fast RCNN type 3 is faster.

[1]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.

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

[3]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  James W. Davis,et al.  Background-subtraction using contour-based fusion of thermal and visible imagery , 2007, Comput. Vis. Image Underst..

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Zhaoqiang Xia,et al.  Multiorientation scene text detection via coarse-to-fine supervision-based convolutional networks , 2018, J. Electronic Imaging.

[7]  Alberto Broggi,et al.  Pedestrian detection in infrared images , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[8]  Jian Sun,et al.  Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Derek Hoiem,et al.  Category Independent Object Proposals , 2010, ECCV.

[10]  Thomas Deselaers,et al.  Measuring the Objectness of Image Windows , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Shiping Ye,et al.  A Novel Pedestrian Detection in Infrared Images , 2016 .

[13]  Wenshu Li,et al.  Pedestrian Detection Based on ISC in Infrared Images , 2012, 2012 Third International Conference on Networking and Distributed Computing.

[14]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[15]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[16]  Andrew Zisserman,et al.  Video Google: a text retrieval approach to object matching in videos , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[17]  Abdenour Hadid,et al.  Unsupervised deep hashing for large-scale visual search , 2016, 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA).

[18]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[19]  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).

[20]  Yafei Zhang,et al.  Target Detection and Pedestrian Recognition in Infrared Images , 2013, J. Comput..

[21]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.