Unsafe Driver Behavior Detection Using Novel Dictionary Algorithm

使用新颖字典算法检测不安全驾驶员行为

• 批准号: RGPIN-2014-03673
• 负责人: Raahemifar, Kaamran
• 金额: $ 1.82万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=662825
• 关键词: Unsafe; Driver; Behavior; Detection; Using

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).

交通事故发生在城市和高速公路上的原因有很多。造成交通事故的一些因素包括驾驶员和行人的情绪状态、疲劳和注意力不集中。本研究计划的短期目标是通过分析驾驶员的眼睛、眉毛和嘴唇来识别驾驶员的面部表情。安装在车内的摄像头网络用于拍摄静态图像。图像处理技术用于提取情感检测所需的面部对象和特征。该研究计划的长期目标是通过安装在十字路口的智能摄像头网络扩展车载摄像头系统，以提醒驾驶员和行人因疲劳或疏忽而产生的潜在危险。这项研究的新奇在于在引入新原子的图像处理中使用了增强的字典方法。情绪/疲劳识别是当图像通过训练算法时基于输入图像的数据库识别各种表情。情绪/疲劳识别的一个应用是基于他/她的状态来识别个体的即将到来的身体反应。恐惧，焦虑，分心，愤怒，注意力不集中和疲劳可能会损害身体的平衡，并影响健康个体在站立和行走时的稳定性。它们也会对个人的反应时间产生负面影响。疲劳是许多导致受伤和死亡的驾驶事故的已知原因。因此，通过人的面部表情检测到的疲劳和情绪变化可以警告即将发生的事故。在这项研究中，我们分析司机的面部表情，以识别潜在的危险情况，并警告司机和行人参与现场。* 通常，识别算法由三个主要步骤组成：1）获取步骤，其中检测个体在表达状态时的伪影; 2）特征提取和表示，其中基于所选择的特征提取方法以几种不同的方式表示所提取的成分;以及3）表情分类步骤，其中使用所提取的特征，该算法确定参与者的最适合的情感。* 这些算法的挑战之一是实现高水平的识别率，低水平的误检（误报）率，以及高灵敏度和特异性率。噪音可能会导致错误识别;例如，一张中性的脸可能被误认为是悲伤的脸，或者一个平静的声音可能被认为是不安的声音。字典学习，特别是当与其他信号处理算法相结合时，已被证明在特征提取中是强大的。我们已经表明，字典算法产生更好的结果时，其他非线性原子被引入到DCT（离散余弦变换）字典。然而，这种增强的方法尚未应用于情绪/疲劳检测，这就是本研究计划的目标。该研究项目的成果是：1）一种增强的图像处理算法，该算法能够以高分类率检测车内驾驶员的情绪状态; 2）用于识别汽车方向和行人疏忽的交叉路口的物体定向检测; 3）车内摄像机系统和交叉路口智能摄像机系统之间的通信协议; 4）驾驶员和行人的警告系统。* 通过开发一种技术来检测驾驶员的不安全行为，并向他们提供适当的警告，该项目促进了道路安全和事故预防。事故减少导致治疗费用（包括康复和事故调查）减少，生产力损失（或缺勤）减少。