Dataset of Annotated Virtual Detection Line for Road Traffic Monitoring

Ivars Namatēvs, Roberts Kadiķis, Anatolijs Zencovs, Laura Leja and Artis Dobrājs
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Ivars Namatēvs: Institute of Electronics and Computer Science, Dzērbenes Str. 14, LV-1006 Riga, Latvia
Roberts Kadiķis: Institute of Electronics and Computer Science, Dzērbenes Str. 14, LV-1006 Riga, Latvia
Anatolijs Zencovs: Institute of Electronics and Computer Science, Dzērbenes Str. 14, LV-1006 Riga, Latvia
Laura Leja: Institute of Electronics and Computer Science, Dzērbenes Str. 14, LV-1006 Riga, Latvia
Artis Dobrājs: Mondot Ltd., Balsta Dambis 80a, LV-1048 Riga, Latvia

Data, 2022, vol. 7, issue 4, 1-7

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.

Keywords: computer vision; multi-object detection; intelligent transportation systems; video surveillance; video analysis (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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