Abstract
This paper introduces blind spot monitoring for vehicles using vehicle mounted cameras in order to reduce traffic accidents. We firstly describe how intelligent surveillance can be used to detect vehicle blind spots by using multiple cameras. We then describe the importance of blind spot detection including technical background. Next, an example is used to explain blind spot and calculate the probability of accidents occurring in the blind spots. After that, a deep learning RNN model is designed to predict the number of cars that will turn up in the blind spots. Finally, our future work and conclusions will be described in the last section. Our contributions in this paper are: (1) data augmentation for deep learning, (2) design a new network for time series analysis, (3) detailed analysis for blind spot detection using the proposed RNN network.
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