H. Gholamalinejad; H. Khosravi
Abstract
In recent years, vehicle classification has been one of the most important research topics. However, due to the lack of a proper dataset, this field has not been well developed as other fields of intelligent traffic management. Therefore, the preparation of large-scale datasets of vehicles for each country ...
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In recent years, vehicle classification has been one of the most important research topics. However, due to the lack of a proper dataset, this field has not been well developed as other fields of intelligent traffic management. Therefore, the preparation of large-scale datasets of vehicles for each country is of great interest. In this paper, we introduce a new standard dataset of popular Iranian vehicles. This dataset, which consists of images from moving vehicles in urban streets and highways, can be used for vehicle classification and license plate recognition. It contains a large collection of vehicle images in different dimensions, viewing angles, weather, and lighting conditions. It took more than a year to construct this dataset. Images are taken from various types of mounted cameras, with different resolutions and at different altitudes. To estimate the complexity of the dataset, some classic methods alongside popular Deep Neural Networks are trained and evaluated on the dataset. Furthermore, two light-weight CNN structures are also proposed. One with 3-Conv layers and another with 5-Conv layers. The 5-Conv model with 152K parameters reached the recognition rate of 99.09% and can process 48 frames per second on CPU which is suitable for real-time applications.
R. Asgarian Dehkordi; H. Khosravi
Abstract
Fine-grained vehicle type recognition is one of the main challenges in machine vision. Almost all of the ways presented so far have identified the type of vehicle with the help of feature extraction and classifiers. Because of the apparent similarity between car classes, these methods may produce erroneous ...
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Fine-grained vehicle type recognition is one of the main challenges in machine vision. Almost all of the ways presented so far have identified the type of vehicle with the help of feature extraction and classifiers. Because of the apparent similarity between car classes, these methods may produce erroneous results. This paper presents a methodology that uses two criteria to identify common vehicle types. The first criterion is feature extraction and classification and the second criterion is to use the dimensions of car for classification. This method consists of three phases. In the first phase, the coordinates of the vanishing points are obtained. In the second phase, the bounding box and dimensions are calculated for each passing vehicle. Finally, in the third phase, the exact vehicle type is determined by combining the results of the first and second criteria. To evaluate the proposed method, a dataset of images and videos, prepared by the authors, has been used. This dataset is recorded from places similar to those of a roadside camera. Most existing methods use high-quality images for evaluation and are not applicable in the real world, but in the proposed method real-world video frames are used to determine the exact type of vehicle, and the accuracy of 89.5% is achieved, which represents a good performance.
