Accurate Vehicle Counting Approach Based on Deep Neural Networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Vehicle counting is considered one of the most important applications in traffic control and management. To count vehicles, synchronous vehicle detection and tracking should be carried out. Recently, detection via deep neural networks (DNN) has achieved good performance. However, exploiting the DNN efficiently for vehicle counting is still challenging. In this paper, an efficient approach for vehicle counting employing DNN and KLT tracker is proposed. To decrease the time complexity, vehicles are detected via DNN every N-frames, N=15 for example. Trajectories are extracted by tracking corner points through the N-frames. Then an efficient algorithm is introduced to assign unique vehicle labels to their corresponding trajectories. The proposed results, performed on diverse vehicle videos, show that vehicles are accurately tracked and counted whatever they are detected one or more times by the DNN.

Original languageEnglish
Title of host publicationProceedings of 2019 International Conference on Innovative Trends in Computer Engineering, ITCE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538652602
DOIs
Publication statusPublished - Feb 20 2019
Externally publishedYes
Event2019 International Conference on Innovative Trends in Computer Engineering, ITCE 2019 - Aswan, Egypt
Duration: Feb 2 2019Feb 4 2019

Publication series

NameProceedings of 2019 International Conference on Innovative Trends in Computer Engineering, ITCE 2019

Conference

Conference2019 International Conference on Innovative Trends in Computer Engineering, ITCE 2019
CountryEgypt
CityAswan
Period2/2/192/4/19

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing
  • Information Systems and Management

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