An Analysis of Background Subtraction on Embedded Platform Based on Synthetic Dataset

Background subtraction is a preliminary technique used for video surveillance and a widely used approach for indexing moving objects. Arange of algorithms have been introduced over the years, and it might be hard to implement the algorithms on the embedded platform because the embedded platform comes up with limited processing power. The goal of this study is to provide a comparative analysis of available background subtraction algorithms on the embedded platform: - Raspberry Pi. The algorithms are compared based on the segmentation quality (precision, recall, and f-measure) and hardware performance(CPU usage and time consumption) using a synthetic video from BMC Dataset with different challenges like normal weather, sunny, cloudy, foggy and windy weather.

[1]  Thierry Chateau,et al.  An Embedded Computer-Vision System for Multi-Object Detection in Traffic Surveillance , 2019, IEEE Transactions on Intelligent Transportation Systems.

[2]  Sakshi Arora,et al.  Morphological based moving object detection with background subtraction method , 2017, 2017 4th International Conference on Signal Processing, Computing and Control (ISPCC).

[3]  Antoine Vacavant,et al.  A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos , 2014, Comput. Vis. Image Underst..

[4]  Chandrika Kamath,et al.  Robust techniques for background subtraction in urban traffic video , 2004, IS&T/SPIE Electronic Imaging.

[5]  Lucky Kodwani,et al.  Automatic Vehicle Detection, Tracking and Recognition of License Plate in Real Time Videos , 2013 .

[6]  Gerhard Rigoll,et al.  Background segmentation with feedback: The Pixel-Based Adaptive Segmenter , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[7]  Marc Van Droogenbroeck,et al.  ViBe: A Universal Background Subtraction Algorithm for Video Sequences , 2011, IEEE Transactions on Image Processing.

[8]  Stefania Perri,et al.  Design of Real-Time FPGA-based Embedded System for Stereo Vision , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[9]  Bob Zhang,et al.  Background modeling methods in video analysis: A review and comparative evaluation , 2016, CAAI Trans. Intell. Technol..

[10]  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).

[11]  Mr. Vaibhav Gandhi,et al.  A Survey: Background Subtraction Techniques , 2015 .

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

[13]  W. Jatmiko,et al.  Improved vehicle speed estimation using Gaussian mixture model and hole filling algorithm , 2013, 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS).

[14]  P. Ramya,et al.  A Modified Frame Difference Method Using Correlation Coefficient for Background Subtraction , 2016 .

[15]  Bin Wang,et al.  A Fast Self-Tuning Background Subtraction Algorithm , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[16]  Chris T. Kiranoudis,et al.  A background subtraction algorithm for detecting and tracking vehicles , 2011, Expert Syst. Appl..

[17]  R.B. Ahmad,et al.  Embedded Smart Camera Performance Analysis , 2009, 2009 International Conference on Computer Engineering and Technology.

[18]  Zoran Zivkovic,et al.  Improved adaptive Gaussian mixture model for background subtraction , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[19]  Wen Hu,et al.  Real-Time and Robust Compressive Background Subtraction for Embedded Camera Networks , 2016, IEEE Transactions on Mobile Computing.

[20]  Lionel Lacassagne,et al.  Motion detection: Fast and robust algorithms for embedded systems , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[21]  Satoshi Goto,et al.  Lossy Compression for Embedded Computer Vision Systems , 2018, IEEE Access.

[22]  Fatih Murat Porikli,et al.  Changedetection.net: A new change detection benchmark dataset , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[23]  Maman Somantri,et al.  Implementation of Vehicle Traffic Analysis Using Background Subtraction in the Internet of Things (IoT) Architecture , 2018, 2018 6th International Conference on Information and Communication Technology (ICoICT).

[24]  Nithin V. George,et al.  An adaptive background subtraction scheme for video surveillance systems , 2017, 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[25]  C. Manaswini.  Efficient Vehicle Counting and Classification using Robust Multi-Cue Consecutive Frame Subtraction , 2013 .