Smaller Object Detection for Real-Time Embedded Traffic Flow Estimation Using Fish-Eye Cameras

Ping Yang Chen, Jun-Wei Hsieh, Munkhjargal Gochoo, Chien Yao Wang, Hong Yuan Mark Liao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Real-time embedded traffic flow estimation (RETFE) systems need accurate and efficient vehicle detection models to meet limited resources in budget, dimension, memory, and computing power. In recent years, object detection became a less challenging task with latest deep CNN-based state-of-the-art models, i.e., RCNN, SSD, and YOLO; however, these models cannot provide desired performance for RETFE systems due to their complex time-consuming architecture. In addition, small object (<30×30 pixels) detection is still a challenging task for existing methods. Thus, we propose a shallow model named Concatenated Feature Pyramid Network (CFPN) that inspired from YOLOv3 to provide above mentioned performance for the smaller object detection. Main contribution is a proposed concatenated block (CB) which has reduced number of convolutional layers and concatenations instead of time-consuming algebraic operations. The superiority of CFPN is confirmed on the COCO and an in-house CarFlow datasets on Nvidia TX2. Thus we conclude that CFPN is useful for real-time embedded smaller object detection task.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages2956-2960
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan
Duration: 22 Sep 201925 Sep 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
CountryTaiwan
CityTaipei
Period22/09/1925/09/19

Keywords

  • edge computing
  • fish-eye camera
  • Small object detection
  • traffic flow estimation
  • YOLOv3

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