Comparison of Vehicle Detection Techniques applied to IP Camera Video Feeds for use in Intelligent Transport Systems.
(چکیده مقاله) :
Abstract :
Vehicle detection is an important area in Transport and Artificial Intelligence. Through vehicle detection techniques, vehicles can
be located across different images. Some of these models are robust enough to identify parts of vehicles in images where the vehicle
might be partially occluded. Recent advances in detection methods gave rise to a range of different techniques that can be used for
recognition and detection of vehicles. Although each technique has its merits, it is not always the case that the adopted model
works well for scenarios involving IP Cameras. The motivation for this study is to compare several state-of-the-art techniques,
including deep learning models and computer vision approaches. A set of experiments are developed in order to test these models
on a number of low quality IP camera footages set in the transport domain in order to measure detection and recognition accuracy.
The final evaluation compares detection accuracy using mean average precision, the semantics of the recognised vehicle as well
as recognition robustness when applied to a dataset that contains images with different light conditions. The study also looks at
persistence in recognition across frames in video data and a detailed description of the dataset used to train the evaluated models.
Finally, the paper also goes through some scenarios that applies the results obtained in this study to ITS systems that use IP camera
feeds
be located across different images. Some of these models are robust enough to identify parts of vehicles in images where the vehicle
might be partially occluded. Recent advances in detection methods gave rise to a range of different techniques that can be used for
recognition and detection of vehicles. Although each technique has its merits, it is not always the case that the adopted model
works well for scenarios involving IP Cameras. The motivation for this study is to compare several state-of-the-art techniques,
including deep learning models and computer vision approaches. A set of experiments are developed in order to test these models
on a number of low quality IP camera footages set in the transport domain in order to measure detection and recognition accuracy.
The final evaluation compares detection accuracy using mean average precision, the semantics of the recognised vehicle as well
as recognition robustness when applied to a dataset that contains images with different light conditions. The study also looks at
persistence in recognition across frames in video data and a detailed description of the dataset used to train the evaluated models.
Finally, the paper also goes through some scenarios that applies the results obtained in this study to ITS systems that use IP camera
feeds
(توضیحات تکمیلی) :
(توضیحات تکمیلی) :
Description :
مقاله ISI انگلیسی اصلی
سال انتشار: 2020
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 8 صفحه
سال انتشار: 2020
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 8 صفحه
Authors / Descriptions(نویسندگان/توضیحات): سال انتشار 2020 \ مقاله ISI انگلیسی اصلی \ نویسندگان: Mark Bugejaa,b,*, Alexiei Dinglib, Maria Attarda, Dylan Seychellb
Sent date(تاریخ ارسال) :
1399/06/09 | 8/30/2020
Number of visits(تعداد بازدید):
540
Key words (کلمات کلیدی):
Intelligent Transport Systems, Computer Vision, Artificial Intelligence, Vehicle Detection;
Number of pages(تعداد صفحات) :
8
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