This paper aims to improve the accuracy of automatic vehicle classifiers for imbalanced datasets. Classification is made through utilizing a single anisotropic magnetoresistive sensor, with the models of vehicles involved being classified into hatchbacks, sedans, buses, and multi-purpose vehicles (MPVs). Using time domain and frequency domain features in combination with three common classification algorithms in pattern recognition, we develop a novel feature extraction method for vehicle classification. These three common classification algorithms are the k-nearest neighbor, the support vector machine, and the back-propagation neural network. Nevertheless, a problem remains with the original vehicle magnetic dataset collected being imbalanced, and may lead to inaccurate classification results. With this in mind, we propose an approach called SMOTE, which can further boost the performance of classifiers. Experimental results show that the k-nearest neighbor (KNN) classifier with the SMOTE algorithm can reach a classification accuracy of 95.46%, thus minimizing the effect of the imbalance.
This paper described a novel vehicle detection sensor design in which dual microwave Doppler radar transceiver modules were used to detect the movement of a parking vehicle. A motion recognition algorithm was also presented to identify the vehicle behavior and generate the parking space occupancy status. Comparing with existing methods such as magnetometer and optical based detection, the proposed design simplified engineering integration from complex optical system design as well as achieved a high detection accuracy. Experimental results showed that the proposed dual microwave Doppler radar sensor detected the vehicle movement clearly and the parking space occupancy detection accuracy was higher than 98%.
SUMMARYWe propose a novel neural networks model based on LSTM which is used to solve the task of classifying inertial sensor data attached to a fence with the goal of detecting security relevant incidents. To evaluate it we deployed an experimental fence surveillance system. By comparing experimental data of different approaches we find out that the neural network outperforms the baseline approach.
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