A comprehensive review of vehicle detection using computer vision

A crucial step in designing intelligent transport systems (ITS) is vehicle detection. The challenges of vehicle detection in urban roads arise because of camera position, background variations, occlusion, multiple foreground objects as well as vehicle pose. The current study provides a synopsis of state-of-the-art vehicle detection techniques, which are categorized according to motion and appearance-based techniques starting with frame differencing and background subtraction until feature extraction, a more complicated model in comparison. The advantages and disadvantages among the techniques are also highlighted with a conclusion as to the most accurate one for vehicle detection

Vehicle make and model recognition system for occlusion and bad lighting images

Intelligent transportation system (ITS) is a massive and very significant sector in the socio-economic context of contemporary society. The need to use roads continues to increase, and this comes with the need to establish more efficient vehicle detection methods. Vehicle Make and Model Recognition (VMMR) has become an important aspect of vision-based systems, since it is applied to access control systems, traffic control, surveillance, and security systems, among others. However, the use of VMMR is challenging due to numerous factors, such as camera angle, poor lighting, and occlusion. Most of the existing works are focused on designing a VMMR system in a normal scenario, where the dataset is set for an ideal scenario, a scenario without illumination, or occlusion. Recent studies have used certain methods to extract the features by extracting the region of interest (ROI) of the front or the rear view of the vehicle to detect and recognize the vehicle. However, the aforementioned methods would fail with poor lighting or occlusion cases. In this thesis, a VMMR system is introduced, which begins by building the dataset, a combination of a benchmark dataset (dataset1) and a self-collected dataset (dataset2). A new approach of image enhancement method was applied to improve the low-light dataset. Then, the enhanced geographical feature extraction techniques were applied to extract the headlight and license plate. For occlusion cases, a new grid-based Speeded-Up Robust Features (SURF) was presented to extract the ROI even in the presence of an occluded object. Two classification approaches were used to recognize the make and model of the vehicle. The first approach is based on the Decision Tree, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) algorithms, where they are called ensemble classifiers, which can predict the VMM accurately. This is because the Decision Tree classifier predicts instances by sorting them based on feature values, while for SVM, the precision of classifying can be enhanced by employing suitable factors. As such, Radial basis function (RBF) kernel and optimized factors were chosen for SVM and KNN, where the testing data was classified by comparison to the k nearest training data based on a distance function. The second approach is the PCANet-II classifier, an approach with second-order pooling and binary feature variance with promising accuracy. The overall performance of the work in this thesis demonstrates a promising outcome, where the overall accuracy reached 96.08% by adopting an ensemble classifier and two datasets (dataset1, dataset2), while the PCANet-II classifier achieved 97.56% using both datasets (dataset1, dataset2). In conclusion, this approach proposed in this thesis showed higher performance than existing methods when bad lighting and occlusion are considered

Voice pathology detection using machine learning technique

Recent proposed researches have witnessed that voice pathology detection systems can effectively contribute to the voice disorders assessment and provide early detection of voice pathologies. These systems used machine learning techniques which are considered as very promising tools in the detection of voice pathologies. However, most proposed systems in the detection of voice disorder utilized limited database. Furthermore, low accuracy rate is still the one of the most challenging issues for these techniques. This paper presents a voice pathology detection system using Online Sequential Extreme Learning Machine (OSELM) to classify the voice signal into healthy or pathological. In this work, the voice features are extracted by using Mel-Frequency Cepstral Coefficient (MFCC). The voice samples for the vowel /a/ were collected equally from Saarbrücken voice database (SVD). The proposed method is evaluated by three widely used measurements which are accuracy, sensitivity and specificity. The obtained results show that the maximum accuracy, sensitivity and specificity are 85%, 87% and 87%, respectively. According to the experimental results, the performance of OSELM algorithm is able to differentiate healthy and pathological voices effectively