Unsafe Driver Behavior Detection Using Novel Dictionary Algorithm

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Accidents happen on the city and highway roads for many reasons. Some of the factors contributing to road accidents include the emotional state, fatigue, and inattentiveness of the drivers and pedestrians. The short-term goal of this research program involves with driver’s facial expression recognition by analyzing their eyes, eyebrows, and lips. A camera network installed inside the car is utilized to take still images. Image processing techniques are used to extract the facial objects and features necessary for emotion detection. The long-term goal of this research program is to expand the in-car camera system with smart-camera networks installed on intersections to alert the drivers and pedestrians of potential dangers due to fatigue or inattentiveness. The novelty of this research is the use of an enhanced dictionary approach in image processing where new atoms are introduced. Emotion/fatigue recognition is the identification of various expressions based on a database of input images when an image is passed through a trained algorithm. One application of emotion/fatigue recognition is in identifying the upcoming physical reaction of an individual based on his/her state. Fear, anxiety, distraction, anger, inattentiveness, and fatigue could compromise the body’s balance, and impact a healthy individual's stability during standing and walking. They also negatively affect the individual’s reaction time. Fatigue is a known cause of many driving accidents resulting in injuries and death. Therefore, fatigue and changes in emotions detected via human's facial expression could alarm an upcoming accident. In this research, we analyze drivers’ facial expressions to identify potentially dangerous conditions and alarm drivers and pedestrians involved in the scene. Typically, recognition algorithms consist of three main steps: 1) the acquisition step in which the artifact of an individual while expressing a state is detected; 2) feature extraction and representation in which the extracted components are represented in several different ways based on the selected feature extraction method; and 3) expression classification step in which using the extracted features, the algorithm determines the best suited emotion of the participant. One of the challenges in these algorithms is to achieve a high level of recognition rate, a low level of misdetection (false alarm) rate, as well as high sensitivity and specificity rates. Noise could result in false recognition; e.g., a neutral face could be mistaken as a sad face, or a calm voice could be identified as a disturbed voice. Dictionary learning, especially when combined with other signal processing algorithms, has proven powerful in feature extraction. We have shown that dictionary algorithm yields better results when other nonlinear atoms are introduced into DCT (discrete-cosine transform) dictionary. However, this enhanced approach has not yet been applied to emotion/fatigue detection and that is what this research program aims at. The deliverables of this research program are: 1) An enhanced image processing algorithm that detects the emotional state of the driver inside a car with high classification rate, 2) object orientation detection for intersections where cars direction, and pedestrian inattentiveness are identified, 3) communication protocol between in-car camera system and intersection smart-camera system, and 4) warning system for both drivers and pedestrians. By developing a technique to detect the unsafe behaviors of drivers and provide them with proper warnings, this project promotes road safety and accident prevention. Fewer accidents leads to reduced cost of treatment (including rehabilitation and incident investigation) and reduced loss of productivity (or absenteeism).