View-independent Vehicle Category Classification System

Abstract

Vehicle category classification is important, but it is a challenging task, especially, when the vehicles are captured from a surveillance camera with different view angles. This paper aims to develop a view-independent vehicle category classification system. It proposes a two-phase system: one phase recognizes the view angles helping the second phase to recognize the vehicle category including bus, car, motorcycle, and truck. In each phase, several descriptors and Machine Learning techniques including traditional algorithms and Deep neural networks are employed. In particular, we used three descriptors: HOG (Histogram of Oriented Gradient), LBP (Local Binary Patterns) and Gabor filter with two classifiers SVM (Support Vector Machine) and k-NN (k-Nearest Neighbor). And also, we used the Convolutional Neural Network (CNN, or ConvNet). Three experiments have been elaborated based on many datasets. The first experiment is dedicated to choosing the best approach for the recognition of views: rear or front. The second experiment aims to classify the vehicle categories based on each view. In the third experiment, we developed the overall system, the categories were classified independently of the view. Experimental results reveal that CNN gives the highest recognition accuracy of 94.29% in the first experiment, and HOG with SVM or k-NN gives the best results (99.58%, 99.17%) in the second experiment. The system can robustly recognize vehicle categories with an accuracy of 95.77%.

Vehicle Make Detection Using the Transfer Learning Approach

“…In 2020, a unique study was carried out to actualize the classification of vehicle models using real surveillance cameras, and the model achieved an accuracy of 62.09 % in real environmental settings [23]. This, together with the increasing number of vehicles and city cameras [7], shows that vehicle classification is relevant and more research is needed to make it adaptive to real-world scenarios.…”

“…The first is the impact of weather, time of day, and varied lighting exposures, all of which can make the model difficult to perform, as the classes are very similar [1]. The lack of open-source datasets that include a range of commonly used vehicles, viewing angles, diverse image quality, and data with more than several images per class is another barrier [7]- [9]. And the third challenge is that newer vehicle models are released on a regular basis, requiring the deep learning model to be retrained with even small amounts of data.…”