Ivars Namatēvs, Roberts Kadiķis, Anatolijs Zencovs, Laura Leja, Artis Dobrājs. Dataset of Annotated Virtual Detection Line for Road Traffic Monitoring. Data, 7(4), 40 pp. MDPI, 2022.
Bibtex citation:
Bibtex citation:
@article{13091_2022,
author = {Ivars Namatēvs and Roberts Kadiķis and Anatolijs Zencovs and Laura Leja and Artis Dobrājs},
title = {Dataset of Annotated Virtual Detection Line for Road Traffic Monitoring},
journal = {Data},
volume = {7},
issue = {4},
pages = {40},
publisher = {MDPI},
year = {2022}
}
author = {Ivars Namatēvs and Roberts Kadiķis and Anatolijs Zencovs and Laura Leja and Artis Dobrājs},
title = {Dataset of Annotated Virtual Detection Line for Road Traffic Monitoring},
journal = {Data},
volume = {7},
issue = {4},
pages = {40},
publisher = {MDPI},
year = {2022}
}
Abstract: Monitoring, detection, and control of traffic is a serious problem in many cities and on roads around the world and poses a problem for effective and safe control and management of pedestrians with edge devices. Systems using the computer vision approach must ensure the safety of citizens and minimize the risk of traffic collisions. This approach is well suited for multiple object detection by automatic video surveillance cameras on roads, highways, and pedestrian walkways. A new Annotated Virtual Detection Line (AVDL) dataset is presented for multiple object detection, consisting of 74,108 data files and 74,108 manually annotated files divided into six classes: Vehicles, Trucks, Pedestrians, Bicycles, Motorcycles, and Scooters from the video. The data were captured from real road scenes using 50 video cameras from the leading video camera manufacturers at different road locations and under different meteorological conditions. The AVDL dataset consists of two directories, the Data directory and the Labels directory. Both directories provide the data as NumPy arrays. The dataset can be used to train and test deep neural network models for traffic and pedestrian detection, recognition, and counting.
Quartile: Q2