Distracted or drowsy driving is unsafe driving behavior responsible for thousands of crashes
every year. Studying driver behavior has challenges associated with observing drivers in …

Driver distraction is one of the main causes of fatal traffic accidents. Therefore, the ability to
detect driver inattention is essential in building a safe yet intelligent transportation system …

In a real-world scenario, human actions are typically out of the distribution from training data,
which requires a model to both recognize the known actions and reject the unknown …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Existing methods for open set action recognition focus on novelty detection that assumes
video clips show a single action, which is unrealistic in the real world. We propose a new …

Open Set Recognition (OSR) is about dealing with unknown situations that were not learned
by the models during training. In this paper, we provide a survey of existing works about …

In real-world scenarios, human actions often fall outside the distribution of training data,
making it crucial for models to recognize known actions and reject unknown ones. However …

Recognizing Activities of Daily Living (ADL) is a vital process for intelligent assistive robots,
but collecting large annotated datasets requires time-consuming temporal labeling and …

Open-world recognition (OWR) is an important field of research that strives to develop
machine learning models capable of identifying and learning new classes as they appear …

Integrating driver, in-cabin, and outside environ-ment's contextual cues into the vehicle's
decision making is the centerpiece of semi-automated vehicle safety. Multiple systems have …