Abnormal Driving Behavior Detection Using Sparse Representation

To reduce the chance of traffic crashes, many driver monitoring systems (DMSs) have been developed. A DMS warns the driver under abnormal driving conditions. However, traditional approaches require enumerating abnormal driving conditions. In this paper, we propose a novel DMS, which models the driver's normal driving statuses based on sparse reconstruction. The proposed DMS compares the driver's statuses with his/her personal normal driving status model and identifies abnormal driving statuses that greatly change the driver's appearances. The experimental results show good performance of the proposed DMS to detect variant abnormal driver conditions.