Enhancing protection of vehicle drivers and road safety by deploying ADAS and Facial Features Pattern Analysis (FFPA) technologies

The latest technology associated with Intelligent Transportation Systems (ITS) have been designed with the aim to minimize the numbers of person injury in road accidents and improve the overall road safety. The driver behavior is one major concern in many accidents in HK urban road links. In particular, the driver\u27s attitudes, such as fatigue, drowsiness and concentration are the major causes to road accidents. It will affect the driver\u27s ability and decisions in properly controlling their vehicles. Very often, this kind of driver distraction is particularly obvious when driving after 2 to 3 hours from most research sources. In the traffic data sourced from Transport Department of HKSAR, around 82% of the personal injury in road accidents belongs to the driver\u27s fault. This paper used the latest technology and applied it to a group of transport vehicles, i.e. taxi. The objective is set up to monitor, record and analyze the fatigue and drowsiness situation of drivers by means of advanced AI system, facial recognition detection system (the sensors) and early warning devices (LDWS) via ADAS technology. The result will be used to give real time early warning and subsequent analysis for the transport operators or researchers for better and safer management of their transport fleets. The system aimed to have a good precaution and protection on all road users, including drivers, passengers and pedestrians. In turn, it largely saves our community resources, such as the medical and social services consumed on treating the injured persons

Eye Blink Detection

Abstract. Nowadays, people spend more time in front of electronic screens like computers, laptops, TV screens, mobile phones or tablets which cause eye blink frequency to decrease. Each blink spreads the tears on the eye cornea to moisture and disinfect the eye. Reduced blink rate causes eye redness and dryness also known as Dry Eye, which belongs to the major symptoms of the Computer Vision Syndrome. The goal of this work is to design eye blink detector which can be used in dry eye prevention system. We have analyzed available techniques for blink detection and designed our own solutions based on histogram backprojection and optical flow methods. We have tested our algorithms on different datasets under various lighting conditions. Inner movement detection method based on optical flow performs better than the histogram based ones. We achieve higher recognition rate and much lower false positive rate than the-state-of-the-art technique presented by Divjak and Bischof

Spatiotemporal CNN with Pyramid Bottleneck Blocks: Application to eye blinking detection

Eye blink detection is a challenging problem that many researchers are working on because it has the potential to solve many facial analysis tasks, such as face anti-spoofing, driver drowsiness detection, and some health disorders. There have been few attempts to detect blinking in the wild scenario, while most of the work has been done under controlled conditions. Moreover, current learning approaches are designed to process sequences that contain only a single blink ignoring the case of the presence of multiple eye blinks. In this work, we propose a fast framework for eye blink detection and eye blink verification that can effectively extract multiple blinks from image sequences considering several challenges such as lighting changes, variety of poses, and change in appearance. The proposed framework employs fast landmarks detector to extract multiple facial key points including the ones that identify the eye regions. Then, an SVD-based method is proposed to extract the potential eye blinks in a moving time window that is updated with new images every second. Finally, the detected blink candidates are verified using a 2D Pyramidal Bottleneck Block Network (PBBN). We also propose an alternative approach that uses a sequence of frames instead of an image as input and employs a continuous 3D PBBN that follows most of the state-of-the-art approaches schemes. Experimental results show the better performance of the proposed approach compared to the state-of-the-art approaches

Outdoor view recognition based on landmark grouping and logistic regression

Vision-based robot localization outdoors has remained more elusive than its indoors counterpart. Drastic illumination changes and the scarceness of suitable landmarks are the main difficulties. This paper attempts to surmount them by deviating from the main trend of using local features. Instead, a global descriptor called landmark-view is defined, which aggregates the most visually-salient landmarks present in each scene. Thus, landmark co-occurrence and spatial and saliency relationships between them are added to the single landmark characterization, based on saliency and color distribution. A suitable framework to compare landmark-views is developed, and it is shown how this remarkably enhances the recognition performance, compared against single landmark recognition. A view-matching model is constructed using logistic regression. Experimentation using 45 views, acquired outdoors, containing 273 landmarks, yielded good recognition results. The overall percentage of correct view classification obtained was 80.6%, indicating the adequacy of the approach.Peer ReviewedPostprint (author’s final draft

高齢者と障害者のための知的福祉システムの開発に関する研究

本論文の構成は以下の通りである。第2 章で脊髄損傷患者や重度肢体不自由者，寝たき りの高齢者などを対象とした眼球の動きと瞬きのみから文字を入力できる装置[11][12] に ついて説明する。第3 章で高齢者が洗い場で転倒した際にシステムが床に設置した可視光センサから得られる光量の変化から自動的に転倒を検知し，第三者に危険を知らせるシス テム[13] を説明し，第4 章でニオイセンサを用いてニオイの変化から独居老人の異常な振 る舞いを検知し，近親者や医師などに異常を知らせるシステム[14] について説明する。第 5 章で高齢者がPC などの情報機器の操作し難い際に操作がし難いことを自動的に機械が 識別し，情報機器の操作を支援するシステム[15] について説明する。第6 で高齢者のカロ リや栄養状態を管理するため，日頃食している料理の画像を撮影し，その料理画像から自 動的に料理を識別し，高齢者の健康管理を補助するシステムについて述べる。最後に第7 章で結論と今後の展望を述べる。金沢大学大学院自然科学研究科博士学位論文, 学位授与年月日:2013年3月26日, 学位授与大学: 金沢大学(2012年度) / 金沢大学大学院自然科学研究科電子情報科学専攻知能情報・数理講

Detecting the Measure of Eye Opening and the Frequency of Blinking in Videosequences

Import 03/11/2016Táto diplomová práca je zameraná na návrh metód detekcie miery otvorenia očí a frekvencie žmurkania. K detekcii stavu oka využívam detekciu dúhovky, očných viečok a klasifikáciu pomocou natrénovaného SVM. Ich kombináciou som zlepšil detekciu žmurkania. Z detekovanej vzdialenosti očných viečok je určená miera otvorenia očí. Navrhnuté metódy sú experimentálne overené na štyroch videách s tromi subjektami pri rôznych svetelných podmienkach. Výsledkom práce je implementácia navrhnutých metód v jazyku C++.This master thesis is focused on designing the methods for detection of the measure of eye opening and frequency of blinking. Detection of iris, eyelids and classification using SVM are used to detect eye states. By combining these detections, eye blinking detection is improved. The measure of eye opening is determined from detected distance of eyelids. Proposed methods were experimentally evaluated in four videosequences of three different subjects in varying lighting conditions. Implementation is done in C++.460 - Katedra informatikyvelmi dobř