Continuous Driver Activity Recognition from Short Isolated Action Sequences

Abstract

Advanced driver monitoring systems significantly increase safety by detecting driver drowsiness or distraction. Knowing the driver's current state or actions allows for adaptive warning strategies or prediction of the driver's response time to take back the control of a semi-autonomous vehicle. We present an online driver monitoring system for detecting characteristic actions and states inside a car interior by analysing the full driver seat region. With the proposed training method, a recurrent neural network for online sequence analysis is capable of learning from isolated action sequences only. The proposed method allows training of a recurrent neural network from snippets of actions, while this network can be applied to continuous video streams at runtime. With a mean average precision of 0.77, we reach better classification results on our test data than commonly used methods.