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Driver cell phone usage detection on Strategic Highway Research Program (SHRP2) face view videos

The harmful effects of cell phone usage on driver behavior have been well investigated and the growing problem has motivated several several research efforts aimed at developing automated cell phone usage detection systems. Computer vision based approaches for dealing with this problem have only emerged in recent years. In this paper, we present a vision based method to automatically determine if a driver is holding a cell phone close to one of his/her ears (thus keeping only one hand on the steering wheel) and quantitatively demonstrate the method's efficacy on challenging Strategic Highway Research Program (SHRP2) face view videos from the head pose validation data that was acquired to monitor driver head pose variation under naturalistic driving conditions. To the best of our knowledge, this is the first such evaluation carried out using this relatively new data. Our approach utilizes the Supervised Descent Method (SDM) based facial landmark tracking algorithm to track the locations of facial landmarks in order to extract a crop of the region of interest. Following this, features are extracted from the crop and are classified using previously trained classifiers in order to determine if a driver is holding a cell phone. We adopt a through approach and benchmark the performance obtained using raw pixels and Histogram of Oriented Gradients (HOG) features in combination with various classifiers.