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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/21711
Title: Trajectory Classification Using Feature Selection by Genetic Algorithm
Authors: Saini R.
Kumar P.
Pratim Roy, Partha
Pal U.
Chaudhuri B.B.
Nakagawa M.
Khanna P.
Kumar S.
Published in: Advances in Intelligent Systems and Computing
3rd International Conference on Computer Vision and Image Processing, CVIP 2018
Abstract: Trajectory classification helps in understanding the behavior of objects being monitored. The raw trajectories may not yield satisfactory classification results. Therefore, features are extracted from raw trajectories to improve classification results. All the extracted features may not be useful for classification. Hence, an automatic selection scheme is essential to find optimal features from the pool of handcrafted features. This paper uses a genetic framework to choose the optimal set of features for trajectory classification purpose. Seven features costing 18 dimensions have been extracted from raw trajectories. Next, Genetic Algorithm (GA) has been used to find the optimal set of features from them. The binary encoding scheme has been used in GA. The 7-bit long chromosomes have been coded in this work. Bits of chromosomes represent trajectory features to be used in classification. Finally, trajectories have been classified using optimal features. Trajectory classification has been done using Random Forest (RF) based classifier and compared with Support Vector Machine (SVM). The results are evaluated using three trajectory datasets, namely I5, LabOmni2, and T15. The classification rates of 99.87%, 93.32%, and 90.58% have been recorded for datasets I5, LabOmni2, and T15, respectively. © 2020, Springer Nature Singapore Pte Ltd.
Citation: Advances in Intelligent Systems and Computing (2020), 1024: 377-388
URI: https://doi.org/10.1007/978-981-32-9291-8_30
http://repository.iitr.ac.in/handle/123456789/21711
Issue Date: 2020
Publisher: Springer Science and Business Media Deutschland GmbH
Keywords: Classification
Genetic algorithm
Random forest
Support vector machine
Surveillance
Trajectory
ISBN: 9.78981E+12
ISSN: 21945357
Author Scopus IDs: 57190288840
36012527200
56880478500
57200742116
Author Affiliations: Saini, R., IIT Roorkee, Roorkee, India
Kumar, P., IIT Roorkee, Roorkee, India
Roy, P.P., IIT Roorkee, Roorkee, India
Pal, U., ISI Kolkata, Kolkata, India
Corresponding Author: Saini, R.; IIT RoorkeeIndia; email: rajkumar.saini@ltu.se
Appears in Collections:Conference Publications [CS]

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