Many automated driver monitoring technologies have been proposed to enhance vehicle and road safety. Most existing solutions involve the use of specialized embedded hardware, primarily in high-end automobiles. This paper explores driver assistance methods that can be implemented on mobile devices such as a consumer smartphone, thus offering a level of safety enhancement that is more widely accessible. Specifically, the paper focuses on estimating driver gaze direction as an indicator of driver attention. Input video frames from a smartphone camera facing the driver are first processed through a coarse head pose direction. Next, the locations and scales of face parts, namely mouth, eyes, and nose, define a feature descriptor that is supplied to an SVM gaze classifier which outputs one of 8 common driver gaze directions. A key novel aspect is an in-situ approach for gathering training data that improves generalization performance across drivers, vehicles, smartphones, and capture geometry. Experimental results show that a high accuracy of gaze direction estimation is achieved for four scenarios with different drivers, vehicles, smartphones and camera locations.

With the prevalence of video recordings from smart phones, dash cams, body cams, and conventional surveillance cameras, privacy protection has become a major concern, especially in light of legislation such as the Freedom of Information Act. Video redaction is used to obfuscate sensitive and personally identifiable information. Today's typical workflow involves simple detection, tracking, and manual intervention. Automated methods rely on accurate detection mechanisms being paired with robust tracking methods across the video sequence to ensure the redaction of all sensitive information while minimizing spurious obfuscations. Recent studies have explored the use of convolution neural networks and recurrent neural networks for object detection and tracking. The present paper reviews the redaction problem and compares a few state-of-the-art detection, tracking, and obfuscation methods as they relate to redaction. The comparison introduces an evaluation metric that is specific to video redaction performance. The metric can be evaluated in a manner that allows balancing the penalty for false negatives and false positives according to the needs of particular application, thereby assisting in the selection of component methods and their associated hyperparameters such that the redacted video has fewer frames that require manual review.

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