Proposal of Segmentation Method Adapted to the Infrared Sensor

In this paper, we show how to use spatial information from infrared sensor images to obtain better segmentation results and how to use temporal information to improve object detection. The proposed method exploits the segmentation results of existing methods to improve the detection of the shape and contour of the moving object in the image. In a comparative study, we use five segmentations from the art’s state to which we add our proposal to obtain good performances. For the very specific images obtained at the output of the infrared sensor, it appears that the use of the thresholding segmentation algorithm combined with our approach allows to better characterize a pixel as a background one or not. The ultimate objective of this work is the exploitation of these results in order to classify the activity of persons present in the scene.

[1]  Thierry Bouwmans,et al.  Recent Advanced Statistical Background Modeling for Foreground Detection - A Systematic Survey , 2011 .

[2]  Lei Liu,et al.  The infrared moving object detection and security detection related algorithms based on W4 and frame difference , 2016 .

[3]  Sukadev Meher,et al.  Detection of Moving Objects Using Fuzzy Color Difference Histogram Based Background Subtraction , 2016, IEEE Signal Processing Letters.

[4]  Kullervo Hynynen,et al.  Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation , 2016, ArXiv.

[5]  Anupam Agrawal,et al.  A survey on activity recognition and behavior understanding in video surveillance , 2012, The Visual Computer.

[6]  Trevor Darrell,et al.  Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Jeng-Shyang Pan,et al.  A Fast Algorithm of Temporal Median Filter for Background Subtraction , 2014, J. Inf. Hiding Multim. Signal Process..

[8]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[9]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Qi Zhang,et al.  Color image segmentation based on a modified k-means algorithm , 2015, ICIMCS '15.

[11]  Pietro Siciliano,et al.  An active vision system for fall detection and posture recognition in elderly healthcare , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[12]  Daijin Kim,et al.  Robust human activity recognition from depth video using spatiotemporal multi-fused features , 2017, Pattern Recognit..

[13]  Dimitrios Makris,et al.  Fall detection system using Kinect’s infrared sensor , 2014, Journal of Real-Time Image Processing.

[14]  I. Haritaoglu,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002 .

[15]  David J. Fleet,et al.  Performance of optical flow techniques , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Shyamanta M. Hazarika,et al.  Comprehensive Representation and Efficient Extraction of Spatial Information for Human Activity Recognition from Video Data , 2016, CVIP.

[17]  Shengping Zhang,et al.  Spatial-temporal nonparametric background subtraction in dynamic scenes , 2009, 2009 IEEE International Conference on Multimedia and Expo.

[18]  L. Davis,et al.  Background and foreground modeling using nonparametric kernel density estimation for visual surveillance , 2002, Proc. IEEE.

[19]  Aini Hussain,et al.  Real-time background subtraction for video surveillance: From research to reality , 2010, 2010 6th International Colloquium on Signal Processing & its Applications.

[20]  Takeo Kanade,et al.  A System for Video Surveillance and Monitoring , 2000 .

[21]  Hélène Laurent,et al.  Evaluating the segmentation result of a gray-level image , 2004, 2004 12th European Signal Processing Conference.