Nitin S. Ujgare; Swati P. Baviskar

An Efficient Object Search in Video Using Template Matching

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Author(s)

Nitin S. Ujgare ^1,* Swati P. Baviskar ²

1. Department of Information Technology, NDMVPS’s KBTCOE, Nashik, 422013, India

2. Government College of Engineering, Aurangabad, 431005, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2019.03.02

Received: 7 Feb. 2018 / Revised: 14 Nov. 2018 / Accepted: 4 Jan. 2019 / Published: 8 Mar. 2019

Index Terms

Object Detection, Spatio-Temporal Trajectories, Template Matching, Video

Abstract

This research paper presents a novel approach for object instance search in video. At the inception, video is selected for which the object instance within the desired video is to be searched and given as an input to system. In preprocessing step, video is divided into key frames. In next step, features are extracted from query image and using template matching algorithm it is compared with key frames. If the object is present in frame then it will display detected object. Similarly, all the frames in video which contains the object are displayed. Max Path Search algorithm is used to remove the noise against classifier and Spatio-Temporal trajectories are used to improve object search. We encountered the fundamental challenge to detect an object from a set of key frames of a video with a partial appearance of object due to lighting, positioning, occlusion etc. from a known class such as logo and any other. The goal of proposed method is to detect all instances of object from known class.

Cite This Paper

Nitin S. Ujgare, Swati P. Baviskar, " An Efficient Object Search in Video Using Template Matching", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.3, pp. 10-17, 2019. DOI: 10.5815/ijigsp.2019.03.02

Reference

[1]Yuning Jiang, Jingjing Meng, Junsong Yuan-“Grid-Based local feature bundling for efficient object search and localization”,18th IEEE International Conference on. Image Processing (ICIP 2011), 113-116. Date. 2011

[2]Jingjing Meng, Junsong Yuan, Gang Wang, Yap-Peng Tan and Jianbo Xu - "Object instance search in videos” Dec 2013, 9th International Conference (ICICS)

[3]JingjingMeng, Jiong Yang, Gang Wang, “Object Instance Search in Videos via Spatio-Temporal Trajectory Discovery”, IEEE TRANSACTIONS on Multimedia, Vol. 18, No. 1, January 2016

[4]Jingjing Meng, Junsong Yuan, Yap-Peng Tan, Gang Wang - "Fast object Instance Search in Videos from One Example.” 27-30 Sept 2015, IEEE International Conference on Image Processing (ICIP), ISBN: 978-1-4799-8339-1

[5]Ran Tao, Efstratios Gavves, Cees G.M. Snoek, Arnold W.M. Smeulders - "Locality in Generic Instance Search from One Example." 23-28 June 2014,Computer Vision and Pattern Recognition (CVPR),2014 IEEE Conference, ISBN: 978-1-4799-5118-5

[6]Yuning Jiang Jingjing Meng Junsong Yuan "Randomized Visual Phrases for Object Search" Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference, 16-21 June 2012, 10.1109/CVPR.2012.6248042

[7]Relja Arandjelovic, Andrew Zisserman - "Three things everyone should know to improve object retrieval.” CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 16-21 June 2012, Pages 2911-2918

[8]Priyank Shah, Hardik Modi,"Comprehensive Study and Comparative Analysis of Different Types of Background Sub-traction Algorithms", IJIGSP, vol.6, no.8, pp.47-52, 2014.DOI: 10.5815/ijigsp.2014.08.07

[9]Pawan Kumar Mishra, G.P Saroha, "A Study on Classification for Static and Moving Object in Video Surveillance System", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.8, No.5, pp.76-82, 2016.DOI: 10.5815/ijigsp.2016.05.07

[10]G.Mallikarjuna Rao, Ch.Satyanarayana,"Object Tracking System Using Approximate Median Filter, Kalman Filter and Dynamic Template Matching", JISA, vol.6, no.5, pp.83-89, 2014. DOI: 10.5815/ijisa.2014.05.09

[11]Prasad Halgaonkar, “Connected Component analysis and Change Detection for Images” IJCTT June Issue 2011, Volume 1(2):224-227 ISSN: 2231-2803.

[12]Wisarut Chantara, Ji-Hun Mun, Dong-Won Shin, and Yo-Sung Ho “Object Tracking using Adaptive Template Matching” IEIE Transactions on Smart Processing and Computing, vol. 4, no. 1, February 2015.

[13]Nazil Perveen, Darshan Kumar and Ishan Bhardwaj “An Overview on Template Matching Methodologies and its Applications” International Journal of Research in Computer and Communication Technology, Vol 2, Issue 10, October- 2013, ISSN (Online) 2278- 5841.

[14]Paridhi Swaroop, Neelam Sharma, “An Overview of Various Template Matching Methodologies in Image Processing”, International Journal of Computer Applications (0975 – 8887) Volume 153 – No 10, November 2016

[15]J. Sivic and A. Zisserman, “Video Google: A text retrieval approach to object matching in videos,” in Proc. IEEE Int. Conf. Comput. Vis., Oct. 2003, vol. 2, pp. 1470–1477.

[16]J. Meng et al., “Interactive visual object search through mutual information maximization,” in Proc. ACM Multimedia, 2010, pp.1147–1150.

[17]Richard J. Radake, Srinivas Andra, “Image Change Detection Algorithms: A Systematic Survey,” IEEE Trans. On Pattern Analysis and Machine Intelligence, vol. 22, no. 3, August 2000

[18]Allen Bovik, “The Essential Guide to Video Processing,” Academic Press- 2nd Edition 2009.

International Journal of Image, Graphics and Signal Processing (IJIGSP)