research-article

A large scale dataset for classification of vehicles in urban traffic scenes

Authors:

Harish S Bharadwaj,

K R RamakrishnanAuthors Info & Claims

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 83, Pages 1 - 8

https://doi.org/10.1145/3009977.3010040

Published: 18 December 2016 Publication History

Abstract

Vehicle Classification has been a well-researched topic in the recent past. However, advances in the field have not been corroborated with deployment in Intelligent Traffic Management, due to non-availability of surveillance quality visual data of vehicles in urban traffic junctions. In this paper, we present a dataset aimed at exploring Vehicle Classification and related problems in dense, urban traffic scenarios. We present our on-going effort of collecting a large scale, surveillance quality, dataset of vehicles seen mostly on Indian roads. The dataset is an extensive collection of vehicles under different poses, scales and illumination conditions in addition to a smaller set of Near Infrared spectrum images for night time and low light traffic surveillance. We will make the dataset available for further research in this area. We propose and evaluate few baseline algorithms for the task of vehicle classification on this dataset. We also discuss challenges and potential applications of the data.

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Cited By

Maity SSingh PKaplun DSarkar R(2024)Current Datasets and Their Inherent Challenges for Automatic Vehicle ClassificationMachine Learning for Cyber Physical System: Advances and Challenges10.1007/978-3-031-54038-7_14(377-406)Online publication date: 12-Apr-2024
https://doi.org/10.1007/978-3-031-54038-7_14
H. El-Saba M(2023)Combining Supervisory Control and Data Acquisition (SCADA) with Artificial Intelligence (AI) as a Video Management SystemIntelligent Video Surveillance - New Perspectives10.5772/intechopen.104766Online publication date: 8-Feb-2023
https://doi.org/10.5772/intechopen.104766
Neun MEichenberger CXin YFu CWiedemann NMartin HTomko MAmbühl LHermes LKopp M(2023)Metropolitan Segment Traffic Speeds From Massive Floating Car Data in 10 CitiesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.329173724:11(12821-12830)Online publication date: Nov-2023
https://doi.org/10.1109/TITS.2023.3291737
Show More Cited By

Index Terms

A large scale dataset for classification of vehicles in urban traffic scenes
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
        Object detection
        Object recognition

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Published In

cover image ACM Other conferences

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

December 2016

743 pages

ISBN:9781450347532

DOI:10.1145/3009977

Program Chairs:
Dhruv Batra
Georgia Tech
,
Michael Brown
York University, Canada
,
Vijay Natarajan
Indian Institute of Science, Bangalore

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Google Inc.
QI: Qualcomm Inc.
Tata Consultancy Services
NVIDIA
MathWorks: The MathWorks, Inc.
Microsoft Research: Microsoft Research

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

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ICVGIP '16

Sponsor:

QI
MathWorks
Microsoft Research

ICVGIP '16: Indian Conference on Computer Vision, Graphics and Image Processing

December 18 - 22, 2016

Assam, Guwahati, India

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ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;

Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Maity SSingh PKaplun DSarkar R(2024)Current Datasets and Their Inherent Challenges for Automatic Vehicle ClassificationMachine Learning for Cyber Physical System: Advances and Challenges10.1007/978-3-031-54038-7_14(377-406)Online publication date: 12-Apr-2024
https://doi.org/10.1007/978-3-031-54038-7_14
H. El-Saba M(2023)Combining Supervisory Control and Data Acquisition (SCADA) with Artificial Intelligence (AI) as a Video Management SystemIntelligent Video Surveillance - New Perspectives10.5772/intechopen.104766Online publication date: 8-Feb-2023
https://doi.org/10.5772/intechopen.104766
Neun MEichenberger CXin YFu CWiedemann NMartin HTomko MAmbühl LHermes LKopp M(2023)Metropolitan Segment Traffic Speeds From Massive Floating Car Data in 10 CitiesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.329173724:11(12821-12830)Online publication date: Nov-2023
https://doi.org/10.1109/TITS.2023.3291737
Ghahremannezhad HShi HLiu C(2023)Object Detection in Traffic Videos: A SurveyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.325868324:7(6780-6799)Online publication date: Jul-2023
https://doi.org/10.1109/TITS.2023.3258683
Kusuma TAshwini K(2023)Real-Time Object Detection and Tracking Design Using Deep Learning with Spatial–Temporal Mechanism for Video Surveillance ApplicationsInnovations in Computer Science and Engineering10.1007/978-981-19-7455-7_56(697-705)Online publication date: 4-May-2023
https://doi.org/10.1007/978-981-19-7455-7_56
Jain NYerragolla SGuha TMohana (2019)Performance Analysis of Object Detection and Tracking Algorithms for Traffic Surveillance Applications using Neural Networks2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC47947.2019.9032502(690-696)Online publication date: Dec-2019
https://doi.org/10.1109/I-SMAC47947.2019.9032502
Paz AGaviria CArteaga CTorres-Jimenez J(2018)Mining Dynamic Network-Wide Traffic States2018 21st International Conference on Intelligent Transportation Systems (ITSC)10.1109/ITSC.2018.8569383(999-1004)Online publication date: Nov-2018
https://doi.org/10.1109/ITSC.2018.8569383
Rohan Phukan JChakrabort S(2018)RDDF: Rating Driven Data Forwarding in Vehicular Network2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)10.1109/ANTS.2018.8710067(1-6)Online publication date: Dec-2018
https://doi.org/10.1109/ANTS.2018.8710067

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