An on-board Android-based smart and safe vehicle monitoring system is presented. The on-board monitoring system (OMS) performs important monitoring functions: Record, Report and Alert (RRA). The Record function records front images of a moving vehicle. During the recording, any accidents or other emergency conditions will be automatically reported via the Report function for an emergency rescue operation. For the detection of shocks or accidents, we use acceleration based shock sensors that utilize fuzzy integral algorithm. The OMS also focuses on drowsiness that is largely regarded as the main cause of most accidents. The Haar-like feature is used to detect any sign of drowsiness and the Alert function is performed to alert the driver. All the vehicle-borne information is stored at a remote server via wireless communication links for later use or post-processing. A test bed has been developed and verified thoroughly for its accurate operations. The proposed smart and safe vehicle monitoring system offers advanced safety features and is expected to substantially reduce fatigue related accidents

Chung, Yeon Ho

Oh, Se-bin

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INT J COMPUT COMMUN, ISSN 1841-98368(4):588-593, August, 2013.Smart and Safe Vehicle Monitoring with Fuzzy Integral andHaar-like FeaturesSe-bin Oh, Yeon-ho ChungSe-bin Oh, Yeon-ho ChungDept. of Information and CommunicationsEngineering, Pukyong National University,Daeyon 3 Dong, Nam Gu, Busan, Koreaosebin@pknu.ac.kr, yhchung@pknu.ac.krAbstract: An on-board Android-based smart and safe vehicle monitoring systemis presented. The on-board monitoring system (OMS) performs important monitor-ing functions: Record, Report and Alert (RRA). The Record function records frontimages of a moving vehicle. During the recording, any accidents or other emergencyconditions will be automatically reported via the Report function for an emergencyrescue operation. For the detection of shocks or accidents, we use acceleration basedshock sensors that utilize fuzzy integral algorithm. The OMS also focuses on drowsi-ness that is largely regarded as the main cause of most accidents. The Haar-likefeature is used to detect any sign of drowsiness and the Alert function is performedto alert the driver. All the vehicle-borne information is stored at a remote server viawireless communication links for later use or post-processing. A test bed has beendeveloped and veriﬁed thoroughly for its accurate operations. The proposed smartand safe vehicle monitoring system oﬀers advanced safety features and is expected tosubstantially reduce fatigue related accidents.Keywords: Fatigue, Haar-like, Monitoring, Vehicle Safety.1 IntroductionOver the last half-decade, smartphones have evolved dramatically and would soon behavelike laptops. Most mobile devices are nowadays integrated by a smartphone as convenient in-frastructure and intelligent functionality has been built to further users ultimate convenience [1].For example, transport machine industry, particularly vehicle industry, provides vehicle-borneinformation via a car navigation system. Even various driving environment information, not tomention information about driving itself, is provided to the driver. However, conventional devicesor modules are focused on data storage and do not oﬀer any drowsiness related safety features.Thus, technologies in mobile and vehicle telematics need to be developed further and also ne-cessitate advanced safety-oriented modules. In particular, a fatigue related accident preventionfeature must be sought together with basic monitoring functions, e.g. recording of vehicle dataand images incorporated with GPS for track-down and investigation of any accidents occurred.This paper addresses a smart and safe vehicle monitoring issue. The system is based on anAndroid mobile device, oﬀering advanced monitoring capability of the recording of vehicle-bornedata and image, GPS data and accelerator sensors for automated alarm function. Also, it oﬀersan alertness function to prevent fatigue related accidents using the Haar-like algorithm [1, 2].2 Design and Structure of OMSThe proposed system is composed of a microcontroller (MCU) module, an Android deviceand database server. Fig. 1 shows a block diagram of the system. Basically, the camera ﬁttedon the mobile device will monitor and record front images. The accelerator sensor will detectshocks or any accidents occurred through the fuzzy integral algorithm [3]. These recorded imagesCopyright c 2006-2013 by CCC PublicationsSmart and Safe Vehicle Monitoring with Fuzzy Integral and Haar-like Features 589will be saved in the SD memory of the mobile device. When required, these will be stored in aremote server via wireless links from the SD memory. When an accident occurs or an emergencycondition exists, a rescue message will automatically be sent to next of kin. The Android-basedmobile development is based on Google Android 2.3 Operating System [4] and some Googlelibraries have been used to implement the proposed functions. The top menu consists of threesubmenus: Camera View, Map View and Accelerometer View.Figure 1: Block diagram of the proposed system2.1 Camera viewCamera View is implemented with a periodical recording function. Various Google librariesare used. For example, recInstallize() is deﬁned for image formatting, encoding modes and thelength of recording, whereas ﬁleOutStream() creates camera recorded image ﬁles and chkAcci-dent() detects an occurrence of accident and recPrepare() sets up a saving route for the ﬁles.Once recorder.start() is invoked, the image recording will begin for the pre-deﬁned time. Whenrecorder.stop() is invoked, the ﬁles will be stored at the SD memory.During this main activity, if the MCU and accelerator sensor receive any data for the possibledetection of accidents or shocks, an alarm dialogue will be sent out and the associated ﬁle willbe automatically saved with a ﬁle extension of Accident.To control an overﬂow issue, old ﬁlesare sequentially deleted while any ﬁles with the extension of Accident remain stored.2.2 Map viewTo obtain location information, a few more libraries are utilized such as onCreate() andgetBestProvider() for the best provider of the information (Wi-Fi, 3G or GPS satellite). Aslocation changes, LocationListener() moves its position and updates its latitude and longitude.With the assumption that the power is supplied from the vehicle, setPowerRequirement() andsetAccuracy() are set to ACCURACY-FINE and POWER-HIGH for a ﬁne collection of the data.All collected data are stored as a text ﬁle and transmitted to a remote database server. Thelocation of the vehicle will be displayed on Google Map online.2.3 Accelerometer viewSensors applied in this development are accelerator sensors, gyroscope and proximity sensors.For the detection of accidents (or shocks), accelerator sensors and GPS speed data as well asshock sensors located inside MCU are employed. Fig. 2(a) shows the directions of vehicle from590 Se-bin Oh, Yeon-ho Chungthe perspective of a mobile placed upright, where Y axis indicates left and right, and Z axis thefront and rear, respectively. Figs. 2(b) and 2(c) show the response of the accelerator sensor whentwo shocks are applied in front and another two from the rear.(a) (b) (c)Figure 2: Accelerometer View: (a) Deﬁnition of vehicle direction (b) and (c) Values of theaccelerometer sensor under shock conditionsAlthough this method of shock detection provides a degree of accuracy, it needs to enhanceits accuracy further, particularly when the vehicle is attacked diagonally or at diﬀerent anglesother than basic four directions. This drawback would result in inaccurate detection of accident.In order to address this ambiguous situation, the study has employed the fuzzy logic and integralalgorithm by treating this condition as fuzziness. Fig. 3 shows accelerator values and its scoperanging from S1 up to S8. Values of Y and Z axes and rule-base of accelerator are shown inFig.4.Figure 3: Block diagram of the proposed system(a) (b)Figure 4: (a) Values of Y and Z axes (b) Rule-base of acceleratorWe ﬁrst deﬁne variables for Y and Z axes and each accelerator value will undergo fuzziﬁcationand defuzziﬁcation. Then, by making use of the center of gravity, the angle of vehicle shock canbe determined. Fig. 5 shows how to ﬁnd the angle of shock using this method.2.4 Methodology of drowsiness detectionIn order to reduce or prevent accidents due to driving under drowsiness, a detection mech-anism of drowsiness has been developed. An eye tracing algorithm known as Haar-like featureSmart and Safe Vehicle Monitoring with Fuzzy Integral and Haar-like Features 591Figure 5: Method of ﬁnding the angle of shock[5] has been used to determine if a driver is in a drowsy condition. The Haar-like features aredigital image features used in object detection and recognition [2, 5]. The method ﬁrst identiﬁesface and eye features and adds pixel values within that area. Then, some weighting factors aremultiplied to ﬁnd a ﬁnal value. For this reason, this feature is useful in moving pictures ratherthan still images.To detect drowsiness, we have used eye closure. This method is well known and widely usedfor detecting drowsiness [6]. When the camera ﬁtted shows an image of a driver, the detectionalgorithm starts to ﬁnd face. Once the face is recognized, the detection for eyes begins to initiatethe eye closure based drowsiness procedure. The method assumes that the drivers eyes are closedif eyes are not detected. In detecting the eye closure, blink count increments and timer starts tomeasure the time during which eyes are closed. Therefore, PERCLOS [6] is computed as follows:PERCLOS =Eye closure timeMeasurement timeX100According to the values of PERCLOS, we determine if the driver is under drowsiness. Thedetailed procedure of the method is shown in Fig. 6.Figure 6: Flow chart of drowsiness detection3 Veriﬁcation of OMSThe OMS has been under rigorous veriﬁcation for its all functions. As mentioned previously,we ﬁrst evaluate the main menu composed of the front image, Google Map and accelerator sensor592 Se-bin Oh, Yeon-ho Chungas shown in Fig.7 (a). When the vehicle is in motion, the accelerator sensor is in operation fordetecting any shocks or accidents. If accidents or shocks occur, an alarm dialog window appearsas shown in Fig.7 (b). In order to avoid any false alarm situations, the dialog window allowsusers to tap OK button to continue the driving, otherwise it regards as an emergency conditionto initiate rescue operations, e.g. call initiation to next of kin as shown in Fig.7(c). In addition,the recorded image ﬁles before and after that moment of the emergency condition are stored withGPS location information into the SD card memory of the mobile device and transmitted to aremote database server via wireless links, e.g. Wi-Fi or 3G. These ﬁles can be viewed anytimeas indicated clearly with an extension of Accident. Figs.7 (d) and 7(e) show the ﬁle storage andGPS data.While the vehicle is in motion, the drivers eyes are continuously monitored. If the value ofPERCLOS is higher than the predeﬁned threshold value, the system is set to send out a warningmessage and an alarm sound also goes oﬀ to alert the driver for the prevention of any possibleaccidents. Fig.8 (a) shows the detection of face and eyes subsequently. The eye closure detectionis then performed and PERCLOS is computed to determine if the driver is in a drowsy conditionas shown in Fig.8 (b).The veriﬁcation process demonstrates that the OMS operates the proposed functions accu-rately as designed.(a) (b)(c) (d)(e)Figure 7: Veriﬁcation of OMS functionsSmart and Safe Vehicle Monitoring with Fuzzy Integral and Haar-like Features 593(a) (b)Figure 8: (a) Detection of face (b) Eye detection for drowsiness4 ConclusionsFuzzy integral and image processing techniques integrated on a smartphone are employedto provide the enhanced safety of vehicle. In addition to providing monitoring functions suchas the Record and Report functions, an advance warning system for fatigue related accidents isdeveloped using the Haar-like feature. The test bed comprising hardware and associated softwarehas been evaluated through its veriﬁcation process. The system shows its accurate and correctoperations on possible driving scenarios. Also, the drowsiness of a driver is successfully detectedusing PERCLOS. It is therefore expected that the developed vehicle monitoring system willplay an essential role in providing enhanced safety feature to transportation machine to reducevehicle-borne accidents signiﬁcantly.Bibliography[1] P. Viola; M. Jones, (2001); Robust real-time object detection, Second International WorkshopOn statistical and computational Theories of Vision Vancouver, Canada.[2] J. Zhou; L. Jiang; L.Shen, (2009);Haar-like features based eye detection algorithm and its im-plementation on TI TMS320DM6446 platform, International Workshop on Imaging Systemsand Techniques.[3] J.M. Mendel (1995); Fuzzy logic systems for engineering: a tutorial, Proc. of IEEEl,83(3):345-377.[4] http://www.developer.android.com[5] Alexander Kuranov; Rainer Lienhart; Vadim Pisarevsky.(2002); An Empirical Analysis ofBoosting Algorithm for Rapid Objects with an Extended Set of Haar-like Features,Intel Tech-nical Report MRL-TR.[6] U.Trustschel; Bo.Sirois; D.Sommer; M.Golz; D.Edwards (2011); PERCLOS: An alertnessmeasure of the pastl, Proc. of the Sixth International Driving Symposium on Human Factorsin Driver Assessment, Training and Vehicle Designl, 172-179.[7] J.Y.Hwang; S.I. Jeong; Y.H.Chung (2011); Development of vehicle motion monitoring modulebased on smartphonel, Journal of Information and Communication Eng, KIICEl, 15(9):1903-1909.[8] http://www.atmel.com

Smart and Safe Vehicle Monitoring with Fuzzy Integral and Haar-like Features

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Smart and Safe Vehicle Monitoring with Fuzzy Integral and Haar-like Features

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