Browsing by Author "Nassuna, Hellen"

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    Feature Selection Based on Variance Distribution of Power Spectral Density for Driving Behavior Recognition
    (IEEE, 2019) Nassuna, Hellen; Eyobu, Odongo Steven; Kim, Jae-Hoon; Lee, Dongik
    Abnormal driving detection and recognition is a crucial area of research towards achieving safety in intelligent transportation systems (ITS). In this study, we propose a feature extraction approach and use the extracted features to train a deep learning model that is used for abnormal driving behavior recognition. The proposed approach derives the features based on variances calculated from each frequency bin containing the power spectrum data that is generated using the short time fourier transform. A subset of features is further selected based on variance similarity of the power spectral data. Similarity is realized by finding intersecting variance data of different variance samples obtained from defined data segments of a given driving behavior class. The driving behaviors considered are weaving, sudden braking and normal driving. Experiments were performed using an artificial neural network to test the efficiency of the proposed feature extraction approach. Results show that an accuracy of 91.0% can be achieved with accelerometer data. The accuracy is further improved to 96.1% by combining accelerometer with gyroscope data.
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    Feature Selection for Abnormal Driving Behavior Recognition Based on Variance Distribution of Power Spectral Density
    (IEMEK Journal of Embedded Systems and Applications, 2020) Nassuna, Hellen; Kim, Jaehoon; Eyobu, Odongo Steven; Lee, Dongik
    The detection and recognition of abnormal driving becomes crucial for achieving safety in Intelligent Transportation Systems (ITS). This paper presents a feature extraction method based on spectral data to train a neural network model for driving behavior recognition. The proposed method uses a two stage signal processing approach to derive time-saving and efficient feature vectors. For the first stage, the feature vector set is obtained by calculating variances from each frequency bin containing the power spectrum data. The feature set is further reduced in the second stage where an intersection method is used to select more significant features that are finally applied for training a neural network model. A stream of live signals are fed to the trained model which recognizes the abnormal driving behaviors. The driving behaviors considered in this study are weaving, sudden braking and normal driving. The effectiveness of the proposed method is demonstrated by comparing with existing methods, which are Particle Swarm Optimization (PSO) and Convolution Neural Network (CNN). The experiments show that the proposed approach achieves satisfactory results with less computational complexity.