Car make and model recognition system using rear-lamp features and convolutional neural networks

Recognizing cars based on their features is a difficult task. We propose a solution that uses a convolutional neural network (CNN) and image binarization method for car make and model classification. Unlike many previous works in this area, we use a feature extraction method combined with a binarization method. In the first stage of the pre-processing part we normalize and change the size of an image. The image is then used to recognize where the rear-lamps are placed on the image. We extract the region and use the image binarization method. The binarized image is used as input to the CNN network that finds the features of a specific car model. We have tested the combinations of three different neural network architectures and eight binarization methods. The convolutional neural network with parameters of the highest quality metrics value is used to find the characteristics of the rear lamps on the binary image. The convolutional network is tested with four different gradient algorithms. We have tested the method on two data sets which differ in the way the images were taken. Each data set consists of three subsets of the same car, but is scaled to different image dimensions. Compared to related works that are based on CNN, we use rear view images in different position and light exposure. The proposed method gives better results compared to most available methods. It is also less complex, and faster to train compared to other methods. The proposed approach achieves an average accuracy of 93,9% on the first data set and 84,5% on the second set

Vehicle make and model recognition using bag of expressions

This article belongs to the Section Intelligent SensorsVehicle make and model recognition (VMMR) is a key task for automated vehicular surveillance (AVS) and various intelligent transport system (ITS) applications. In this paper, we propose and study the suitability of the bag of expressions (BoE) approach for VMMR-based applications. The method includes neighborhood information in addition to visual words. BoE improves the existing power of a bag of words (BOW) approach, including occlusion handling, scale invariance and view independence. The proposed approach extracts features using a combination of different keypoint detectors and a Histogram of Oriented Gradients (HOG) descriptor. An optimized dictionary of expressions is formed using visual words acquired through k-means clustering. The histogram of expressions is created by computing the occurrences of each expression in the image. For classification, multiclass linear support vector machines (SVM) are trained over the BoE-based features representation. The approach has been evaluated by applying cross-validation tests on the publicly available National Taiwan Ocean University-Make and Model Recognition (NTOU-MMR) dataset, and experimental results show that it outperforms recent approaches for VMMR. With multiclass linear SVM classification, promising average accuracy and processing speed are obtained using a combination of keypoint detectors with HOG-based BoE description, making it applicable to real-time VMMR systems.Muhammad Haroon Yousaf received funding from the Higher Education Commission, Pakistan for Swarm Robotics Lab under the National Centre for Robotics and Automation (NCRA). The authors also acknowledge support from the Directorate of ASR& TD, University of Engineering and Technology Taxila, Pakistan