Autonomous driving object detection using ACF M. J. Mohammed, S. V. Shidlovskiy
Material type:
ArticleContent type: Текст Media type: электронный Subject(s): автономное транспортное средство | глубокое обучение | обнаружение объектов | семантическая сегментацияGenre/Form: статьи в сборниках Online resources: Click here to access online
In:
Инноватика-2021 : сборник материалов XVII Международной школы-конференции студентов, аспирантов и молодых ученых, 22-23 апреля 2021 г., г. Томск, Россия С. 164-167Abstract: In this work, we obtain a vehicle semantic understanding using their image features and a rule-based system. These features provide the vehicle spatial and temporal information. Vehicle spatial feature is obtained using an ACF network. The vehicle temporal information is obtained using a novel semantic segmentation framework. The statuses of the neighboring vehicles are categorized as «Carfollow » and «Car-avoid» also. We validate our proposed frame-work with multiple acquired sequences. Our experimental results show that the proposed framework can estimate the status of the different vehicles in the urban road environment in near real-time.
Библиогр.: 3 назв.
In this work, we obtain a vehicle semantic understanding using their image features and a rule-based system. These features provide the vehicle spatial and temporal information. Vehicle spatial feature is obtained using an ACF network. The vehicle temporal information is obtained using a novel semantic segmentation framework. The statuses of the neighboring vehicles are categorized as «Carfollow » and «Car-avoid» also. We validate our proposed frame-work with multiple acquired sequences. Our experimental results show that the proposed framework can estimate the status of the different vehicles in the urban road environment in near real-time.
There are no comments on this title.