Performance Evaluation of Feature Extraction Algorithms for Vehicle Shape Classification

Vehicle classification is a classic application of automotive image processing that is necessary for a variety of modern vehicle safety and comfort features. While machine learning-based solutions are effective in these fields and are currently employed extensively in various automotive applications. However, the most challenging aspect of automotive image processing with machine learning methods is gathering adequate quality and quantity datasets to develop such applications. Additionally, imbalanced datasets are common in multiclass automotive image processing, as is the case with the current topic of vehicle platoon management. The effectiveness of available handcrafted feature extractors and classifiers employed for vehicle class categorization varies greatly due to the effect of dataset imbalance. This study aims to examine how the performance of four prominent feature extractors alters when used with the imbalance dataset for vehicle shape classification. Also, the use of image augmentation techniques to increase the dataset size for three vehicle classes: car, bus, and truck, has been presented. Further, using the Support Vector Machine (SVM) classifier, experimental analysis was performed using feature extractors such as Histogram of Oriented Gradient (HOG), Scaled Invariant Feature Transform (SIFT), Speeded-Up Robust Feature (SURF), and Haar. Vehicle shape classification, which is an important characteristic in vehicle platoon management, has been evaluated using Receiver Operating Characteristic (ROC) for both the unbalanced dataset and the augmented dataset. The experimental results demonstrate that using the HOG feature extractor performs better when compared to SIFT, SURF, and HAAR feature extractors on the imbalance dataset. After using an image augmentation technique to add images, output performance improved significantly, with HOG output of 95%, SIFT output of 91%, SURF output of 91%, and HAAR output of 96%.