Human Distraction Detection from Video Stream Using Artificial Emotional Intelligence
Rafflesia Khan, Rameswar Debnath · International Journal of Image Graphics and Signal Processing · 2020
This paper addresses the problem of identifying certain human behavior such as distraction and also predicting the pattern of it.This paper proposes an artificial emotional intelligent or emotional AI algorithm to detect any change in visual attention for individuals.Simply, this algorithm detects human's attentive and distracted periods from video stream.The algorithm uses deviation of normal facial alignment to identify any change in attentive and distractive activities, e.g., looking to a different direction, speaking, yawning, sleeping, attention deficit hyperactivity and so on.For detecting facial deviation we use facial landmarks but, not all landmarks are related to any change in human behavior.This paper proposes an attribute model to identify relevant attributes that best defines human's distraction using necessary facial landmark deviations.Once the change in those attributes is identified, the deviations are evaluated against a threshold based emotional AI model in order to detect any change in the corresponding behavior.These changes are then evaluated using time constraints to detect attention levels.Finally, another threshold model against the attention level is used to recognize inattentiveness.Our proposed algorithm is evaluated using video recording of human classroom learning activity to identify inattentive learners.Experimental results show that this algorithm can successfully identify the change in human attention which can be used as a learner or driver distraction detector.It can also be very useful for human distraction detection, adaptive learning and human computer interaction.This algorithm can also be used for early attention deficit hyperactivity disorder (ADHD) or dyslexia detection among patients.