Proposed novel in-vehicle occupancy detection algorithm.
Abstract:
We propose a novel algorithm to identify occupied seats in a motor vehicle, i.e., the number of occupants and their positions, using a frequency modulated continuous wave...Show MoreMetadata
Abstract:
We propose a novel algorithm to identify occupied seats in a motor vehicle, i.e., the number of occupants and their positions, using a frequency modulated continuous wave radar. Instead of using a high-resolution radar, which increases the cost and device size, and performing complex signal processing with several variables to be tuned for each scenario, we integrate machine learning algorithms with a low-cost radar system. Based on heat maps obtained from the Capon beamformer, we train a machine classifier to predict the number of occupants and their positions in a vehicle. We follow two different classification methods: multiclass classification and binary classification. We compare three classifiers: support vector machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), in terms of accuracy and computational complexity for both testing and training sets. Our proposed system using an SVM classifier achieved an overall accuracy of 97% in classifying the defined scenarios in both multiclass classification and binary classification methods. In addition, to show the effectiveness of our proposed in-vehicle occupancy detection method, we provide the results of a commonly available people counting and tracking method for occupancy detection. Compared to common methods, the effectiveness, robustness, and accuracy of our proposed in-vehicle occupancy detection method are demonstrated.
Proposed novel in-vehicle occupancy detection algorithm.
Published in: IEEE Access ( Volume: 10)
Funding Agency:
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- IEEE Keywords
- Index Terms
- Machine Learning ,
- Heatmap ,
- Training Set ,
- Learning Algorithms ,
- Signal Processing ,
- Computational Complexity ,
- Support Vector Machine ,
- Random Forest ,
- Classification Methods ,
- Binary Classification ,
- K-nearest Neighbor ,
- Multi-label ,
- Support Vector Machine Classifier ,
- Counting Method ,
- Radar System ,
- Frequency Modulated Continuous Wave ,
- Wave Radar ,
- People Counting ,
- High-resolution Radar ,
- Low Resolution ,
- Angle Of Arrival ,
- Random Forest Classifier ,
- Radar Processing ,
- Radar Sensor ,
- Angular Resolution ,
- Radar Techniques ,
- Constant False Alarm Rate ,
- Point Cloud ,
- Virtual Channel ,
- Results Of Algorithm
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Machine Learning ,
- Heatmap ,
- Training Set ,
- Learning Algorithms ,
- Signal Processing ,
- Computational Complexity ,
- Support Vector Machine ,
- Random Forest ,
- Classification Methods ,
- Binary Classification ,
- K-nearest Neighbor ,
- Multi-label ,
- Support Vector Machine Classifier ,
- Counting Method ,
- Radar System ,
- Frequency Modulated Continuous Wave ,
- Wave Radar ,
- People Counting ,
- High-resolution Radar ,
- Low Resolution ,
- Angle Of Arrival ,
- Random Forest Classifier ,
- Radar Processing ,
- Radar Sensor ,
- Angular Resolution ,
- Radar Techniques ,
- Constant False Alarm Rate ,
- Point Cloud ,
- Virtual Channel ,
- Results Of Algorithm
- Author Keywords