Video Traffic Volume Extraction Based on Onelevel Feature

In this paper, a single-level feature detector network based on YOLOF is built to detect objects and extract traffic volume information for videos. YOLOF does not have a multi-scale feature fusion structure (such as FPN, PAN). It uses the dilated encoder and uniform matching method to replace the fu...

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Bibliographic Details
Main Authors: Teng, Da, Xie, Xingsheng, Sun, Jiejie
Format: Conference Proceeding
Language:English
Subjects:
Backbone
Deep learning
Detectors
Feature extraction
Mechatronics
Sensitivity
Target tracking
traffic volume
Training
Video object detection
Video sequences
Yolof
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Description
Summary:In this paper, a single-level feature detector network based on YOLOF is built to detect objects and extract traffic volume information for videos. YOLOF does not have a multi-scale feature fusion structure (such as FPN, PAN). It uses the dilated encoder and uniform matching method to replace the fusion feature module, which simplifies the network structure and improves the response time while maintaining good accuracy. Finally, the target tracking algorithm is used to obtain the traffic flow statistics in the video sequence. Generally speaking, YOLOF has higher detection performance and faster speed than RetinaNet, DETR and other networks of similar structure.
ISSN:2693-289X
DOI:10.1109/ITOEC53115.2022.9734413