Faster CNN-based vehicle detection and counting strategy for fixed camera scenes

Ahmed Gomaa, Tsubasa Minematsu, Moataz M. Abdelwahab, Mohammed Abo-Zahhad, Rin ichiro Taniguchi

研究成果: ジャーナルへの寄稿学術誌査読

2 被引用数 (Scopus)

抄録

Automatic detection and counting of vehicles in a video is a challenging task and has become a key application area of traffic monitoring and management. In this paper, an efficient real-time approach for the detection and counting of moving vehicles is presented based on YOLOv2 and features point motion analysis. The work is based on synchronous vehicle features detection and tracking to achieve accurate counting results. The proposed strategy works in two phases; the first one is vehicle detection and the second is the counting of moving vehicles. Different convolutional neural networks including pixel by pixel classification networks and regression networks are investigated to improve the detection and counting decisions. For initial object detection, we have utilized state-of-the-art faster deep learning object detection algorithm YOLOv2 before refining them using K-means clustering and KLT tracker. Then an efficient approach is introduced using temporal information of the detection and tracking feature points between the framesets to assign each vehicle label with their corresponding trajectories and truly counted it. Experimental results on twelve challenging videos have shown that the proposed scheme generally outperforms state-of-the-art strategies. Moreover, the proposed approach using YOLOv2 increases the average time performance for the twelve tested sequences by 93.4% and 98.9% from 1.24 frames per second achieved using Faster Region-based Convolutional Neural Network (F R-CNN) and 0.19 frames per second achieved using the background subtraction based CNN approach (BS-CNN), respectively to 18.7 frames per second.

本文言語英語
ページ(範囲)25443-25471
ページ数29
ジャーナルMultimedia Tools and Applications
81
18
DOI
出版ステータス出版済み - 7月 2022

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • メディア記述
  • ハードウェアとアーキテクチャ
  • コンピュータ ネットワークおよび通信

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