Skip navigation
Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/15675
Title: An efficient approach for trajectory classification using FCM and SVM
Authors: Saini R.
Kumar P.
Pratim Roy, Partha
Dogra D.P.
Published in: Proceedings of TENSYMP 2017 - IEEE International Symposium on Technologies for Smart Cities
Abstract: Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera and Global Positioning System (GPS) based monitoring are one of the key parts of it. Filtering or classification of infrequent or anomalous activities in traffic data help to understand the flow of movements in monitoring area. Video based surveillance involves the extraction of object trajectories from videos and then analyzing them to spot unusual behavior of objects to secure area under surveillance. In this paper, we propose an efficient approach for the classification of object trajectories using the combination of Fuzzy C-Means (FCM) clustering technique and Support Vector Machine (SVM). The features extracted from FCM are then classified using SVM classifier. The approach has been tested on two publicly available datasets, namely, CROSS [12] and T11 [18]. Accuracies of 90.37% and 87.29% have been recorded on CROSS and T11 datasets, respectively. The combined approach outperforms the traditional SVM based classification on these datasets. © 2017 IEEE.
Citation: Proceedings of TENSYMP 2017 - IEEE International Symposium on Technologies for Smart Cities, (2017)
URI: https://doi.org/10.1109/TENCONSpring.2017.8070076
http://repository.iitr.ac.in/handle/123456789/15675
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers Inc.
Keywords: Classification
Clustering
Fuzzy C-Means
Support Vector Machines
Trajectory
ISBN: 9.78151E+12
Author Scopus IDs: 57190288840
57212043589
56880478500
35408975400
Author Affiliations: Saini, R., Department of CSE, Indian Institute of Technology, Roorkee, India
Kumar, P., Department of CSE, Indian Institute of Technology, Roorkee, India
Roy, P.P., Department of CSE, Indian Institute of Technology, Roorkee, India
Dogra, D.P., School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India
Appears in Collections:Conference Publications [CS]

Files in This Item:
There are no files associated with this item.
Show full item record


Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.