Obstacle Avoidance for Visually Impaired Using Auto-adaptive Thresholding on Kinect s Depth Image

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1 Obstacle Avoidace for Visually Impaired Usig Auto-adaptive Thresholdig o Kiect s Depth Image Muhamad Risqi Utama Saputra, Widyawa, Paulus Isap Satosa Departmet of Electrical Egieerig ad Iformatio Techology Uiversitas Gadjah Mada, Yogyaarta, Idoesia Abstract Visually impaired people eed assistace to avigate safely, especially i idoor eviromet. This research developed a obstacle avoidace system for visually impaired usig Kiect s depth camera as the mai visio device. A ew approach called auto-adaptive thresholdig is proposed to detect ad to calculate the distace of obstacle from the user. The proposed method divides equally a depth image ito three areas. It fids the most optimal threshold value automatically (auto) ad vary amog each of those areas (adaptive). Based o that threshold value, the distace of the closest obstacle for each area is determied by average fuctio. To respod the existece of the obstacle, the system gives soud ad voice feedbac to the user through a earphoe. The experimetal result shows that executio time ad error of the system i calculatig the distace of the obstacle are 1.4 ms ad mm respectively. Evaluatio with blid-folded persos idicates that the system could successfully guide them to avoid obstacles i real-time coditio. Keywords obstacle avoidace; visually impaired; autoadaptive thresholdig; assistive techology; wearable device; I. INTRODUCTION Based o World Health Orgaizatio (01), there are 85 millios visual impairmets i the world, which 39 millios of them are completely blid [1]. These people highly eed assistat to carry out daily activities. Oe of the most difficult activities that must be coducted by visually impaired is idoor avigatio. I idoor eviromet, visually impaired should be aware of obstacles i frot of them ad be able to avoid it. To help them to avigate safely, most of them use white cae or guide dog. White cae could explore eviromet at average distace 1.5 meter []. However, white cae has some drawbacs, amely (1) white cae ability to explore eviromet is limited to its legth ad oly at positio where the user poitig the cae (the other positio is blid spot) ad () white cae ca t show automatically which path that free from obstacles. O the other had, the guide dog ca show the user where the free path is, but it s expesive [3] ad eed to be traied. Based o those reasos, the eed to develop Assistive Techology (AT) to aid visually impaired i avoidig obstacle is quite high. O may studies, researchers have developed AT based o laser or ultrasoic sesor that was fused with a cae. Wahab, et al. [4] combie a cae with ultrasoic sesor ad voice alert system to create Smart Cae. Smart Cae emits ultrasoic sigal ad calculates the time iterval betwee sedig the sigal ad receivig the echo to determie the distace of obstacle. The system the alerts the user usig voice feedbac based o three differet types of distace: far, medium, ad close. But, sometimes the voice feedbac is too repetitive if it s ot hadled properly, so it maes the user cofused. To avoid this shortcomig, istead of usig voice feedbac, Mitsuhiro Oayasu [5] uses vibrator to alert the user whe a obstacle is detected. Aother research about AT for visually impaired was coducted by Bejami [6]. He developed a tool called C-5 Laser Cae, a cae that is equipped with laser techology. C-5 Laser Cae emits pulses of ifrared light ad catches bac the reflected sigal by a photodiode placed behid a receivig les. Based o the agle made by the diffuse reflected ray, the Triagulatio method is used to calculate the distace of the object. To otify the user about detectio of obstacle, the system will sigal the user with a high-pitched beep. Resemblig what Bejami did, Ahlmar, et al. [7] use laser as well to determie the distace of the object, but the system is icorporated with a wheelchair to help blid people with motio impairmet. The laser ragefider itself is viewed as a virtual white cae. Not oly that, the system also utilizes haptic techology (techology of the sese of touch) to iform the user about eviromet that has bee explored. May other researchers also used laser ad ultrasoic, but istead of combiig it with a cae, they merged it with wearable device [8], [9] or a robot [10]. Besides of laser ad ultrasoic techology, recetly, researchers have bee startig to use depth camera as a optio of AT. It has bee happeig sice the affordable depth camera, such as Microsoft Kiect ad Asus Xtio, was available i the maret. Depth camera ca be used to detect obstacle because it provides a image that cotais depth iformatio o each pixel, but it eeds further process to be able to do that. Steve Ma, et al. [11] use 1-to-1 ceterweighted mappig o depth image to develop collisio avoidace system. The system divides depth image ito 6 areas ad it maps the weighted distace of each area with vibratio to iform the user about the eviromet. The closer the distace is, the stroger the vibratio is, so the user ca avoid

2 obstacle whe the vibratio at specific area become stroger tha the others. Aother idetical research coducted by D. Berabei, et al. [1] utilize depth camera to fid the farthest poit reachable by the visually impaired cosiderig his/her height ad width. The system produces low-resolutio depth map cotaied quatizatio of the space i frot of the user ito the object i the scee. Based o that map, the system decides what feedbac to give to the user, for example to chage directio or to stop. Usig differet approach, Atif Kha, et al. [3] also use depth camera but the system splits depth image ito 15 areas ad views each area ito metric of obstacle. He created a recommedatio system based o the smallest probability that a area has a obstacle. To improve efficiecy, the system trasforms 640x480 pixels of depth image ito 3x40 pixels by calculatig average depth value i each bloc. The, the feedbac is give to the user through voice message usig textto-speech techology. I order to fid the closest object, Zoller, et al. [13] use movig depth widow to examie whether a area i depth histogram exceeds certai threshold area or ot. If the observed area surpasses 4% of a regio, the system iform to the user through vibrotactile waist belt. Aother method implemets marchig squares algorithm o depth image to detect obstacles [14]. The system wors cotiuously by performig dow-samplig of depth data ito a low-resolutio image, splittig depth data ito isolated structures at differet depth level, ad soifyig obstacle iformatio to the user s headphoe. The objective of this research is similar with what was doe by researchers metioed above: to develop collisio avoidace system for visually impaired. But, this research proposes a ew approach to detect ad to calculate the distace of obstacle usig auto-adaptive thresholdig o depth image. Depth camera was chose i this research because it s ot expesive ad easy to implemet. The desig ad cofiguratio of the system will be covered i the ext sectio. The third sectio will explais about collisio avoidace method. The, the fourth sectio shows the experimetal result. Fially, the last sectio sums up the result ad discusses further research. II. SYSTEM CONFIGURATION The compoets of the system cosist of 3 mai parts: (1) depth camera, () oteboo/computer tablet with USB hub, ad (3) earphoe. Depth camera that is used i this research is Microsoft Kiect. Kiect provides depth image with 640x480 resolutio ad 30 frame rates/secod. The physical limits of Kiect to measure depth iformatio is withi rage of 0.8 meter to 4 meter, with horizotal ad vertical agle of visio are ad respectively [15]. This physical limits is cosidered suitable to develop collisio avoidace system because it will give eough time for visually impaired to avoid a obstacle whe it s detected at that rage. I order to alert the user about the obstacle, the system will iform it through a earphoe. Fig. 1 depicts how these 3 mai parts will be used by visually impaired. Kiect is placed i frot of stomach lie a belt so that it ca reach object at floor ad at huma high. Kiect 1 3 Earphoe Laptop Fig. 1. The prototype of the system cosists of 3 mai parts: (1) Microsoft Kiect, () laptop, ad (3) earphoe. The blid-folded ma i this picture employs the system while coductig experimet. Fig. describes data flow betwee Kiect, laptop, ad the user. Kiect trasmits raw depth data to the system i the computer. The, the system will process the raw depth data ad coverts it ito meaigful iformatio, amely soud alert otificatio ad voice recommedatio. Fially, the system seds those iformatio to the user through a earphoe. Microsoft Kiect Raw Depth Data Noteboo/ Tablet Voice Recommedatio Soud Notificatio User Fig.. Data flow betwee Kiect, computer, ad visually impaired. III. A. Basic Priciple OBSTACLE AVOIDANCE 1 RGB Image Depth Image 3 Depth Histogram Fig. 3. The correlatio betwee (1) colour image, () depth image, ad (3) depth histogram. The area of depth histogram surrouded by blac circle idicates the object (chair) i depth image. The idea behid the proposed method i this research is depicted i Fig. 3. As previously metioed, depth image provides depth iformatio i each pixel ad it ca be see as depth histogram. Fig. 3 describes the correlatio betwee colour image, depth image, ad depth histogram. Based o the picture, there are mai objects with differet distace (idicated by differet colour i depth image): wall ad chair. If a image composed by those objects ad it s coverted ito depth histogram, the result is peas with differet local maxima, oe correspodig to each object. So, the poit is, a

3 local maximum i the depth histogram geerally meas a object. I order to fid the closest object, the process is simply to set a threshold value that will separate the closest object ad aother objects behid it. But, this method does t always wor whe the scee o the picture is too complicated ad cluttered. Therefore, this research used auto-adaptive thresholdig method to deal with it. B. Auto-adaptive Thresholdig Auto-adaptive thresholdig o depth image refers to a method that ca geerate the most optimal threshold value automatically (auto) ad vary amog each differet area of depth image (adaptive). This method yields more tha oe threshold values because sometimes it is too difficult to isolate the closest object with oly sigle threshold value. So, depth image will be divided ito several areas ad each area has its threshold value. The complete process is described i Fig. 4. Start ) Dividig Depth Image ito 3 Areas The secod step is dividig depth image ito 3 equal areas (as show i Fig. 5), that is, left area, middle area, ad right area. Each area of depth image is associated with the path that ca be traversed by visually impaired whe the system is performig obstacle avoidace recommedatio. For example, whe there s a obstacle i the middle area, the system gives recommedatio to move to the right area to avoid it. The choice to divide depth image oly ito 3 areas is that the movemets of visually impaired is imprecise [1], so that with oly 3 optios, it will mae visually impaired uderstad ad execute easily the commad from the system. This process to divide depth image ito 3 areas ad other processes after it will be carried out twice per secod. It is doe to avoid burdeig the system excessively. As previously metioed, each area will has its threshold value. So, the ext step will be processed separately to each area. Depth Image Acquisitio No Is the umber of frames ca be divided by 15? Pea Detectio ad Selectio Yes Dividig Depth Image Ito 3 Areas Dow-samplig ad Depth Histogram Coversio Threshold Selectio Usig Otsu Method Distace Calculatio Fiish Fig. 4. The flowchart of auto-adaptive thresholdig method. The output of this method is the distace of the closest object from the user (i millimeter). 1) Depth Image Acquisitio Auto-adaptive thresholdig begis by performig depth image acquisitio from Kiect. Raw data from Kiect cotais depth iformatio for each pixel ad it eed to be coverted ito 8-bit grayscale image to visualize it ito visible image. The coversio process is performed by equatio (1): ( max( d 800,0) ) 55 i = 55 (1) 300 where i is the th pixel i grayscale image ad d is the th depth iformatio i depth image. I order to reduce high error because of low accuracy of Kiect at rage closer tha 800 mm ad further tha 4000 mm, the value beyod that rage will be chaged to 0 mm by equatio (). d, if 800 d 4000 d = 0, if d < 800, d > 4000 () Fig. 5. Depth image is divided ito 3 areas: left area, middle area, ad right area. Each area represets the path that ca be traversed by visually impaired. 3) Dow-samplig ad Depth Histogram Coversio Dow-samplig is coducted to accelerate computig process ad to reduce iefficiecy because of processig excessive uecessary iformatio of depth image. For each x pixel area o depth image, oly 1 pixel will be used. So, the total umber of pixels that will be processed is about 1900 pixels. The, the group of data from each depth image area is coverted ito depth histogram by classifyig it ito 100 groups (the iterval betwee each group is 40 mm). 4) Pea Detectio ad Selectio To discover objects i each area of depth image, the system must be able to fid the local maxima i each depth histogram. Each of local maximum ca be foud by usig cotrast fuctio as show i equatio (3): i+ = i i 1 = i i+ cotrast( i, ) = p p p (3) = i+ + 1 where i is the observed positio, is the parameter for addig up the cotrast of the pea ad its eighbour, ad p is the value i positio of i the depth histogram. The umber is used to filter the oise ad uexpected local pea positios [16]. The value of that is used i this research is 1.5. This umber is determied experimetally.

4 All of local maxima discovered by cotrast fuctio is ot always represet the object. To determie the object, the cotrast value of local maximum has to meet these criterias: The miimum distace betwee oe local maximum with aother oe i depth histogram is 4 (positive/egative), otherwise it will be treated as a part of a object. The miimum score of cotrast value is 50, otherwise the system will cosider it too small to be a obstacle. This umber is determied based o observatio. After all of local maxima is foud, the system will choose the two closest oe from the Kiect (as show i Fig. 6). Oly these two local maxima that will be processed i the ext step. class C 0, ad μ 1 is the mea of class C 1. The value of μ T ca be easily computed by equatio (6) because μ T is the overall mea of the whole image. μ + T = ω0μ0 ω1μ1 (6) This Otsu method is used to determie the best threshold value betwee two local maxima that is geerated by pea detectio ad selectio method described i the previous step. I this case, the C 0 class of Otsu method is most liely the part of depth histogram that belogs to the closest object whereas the C 1 class belogs to aother object behid it. After this step, the system eables to separate the closest object at each area of depth image with other objects behid it. Fig. 6. The two closest local maxima from Kiect is mared usig red circle. The others is mared with blue circle. The local maxima that will be processed i the ext step is oly those two closest oe. 5) Threshold Selectio Usig Otsu Method I order to fid the most optimal threshold value automatically, the system will use Otsu Thresholdig method. Otsu Thresholdig is oe of the oldest ad the best automatic thresholdig method [17]. It is usupervised method which was developed by Nobuyui Otsu i Based o Otsu method, the best threshold value is determied by discrimiat criteria, that is, to maximize the separability betwee two classes which are yielded by the threshold value. It is doe by fidig the maximum variace betwee two classes (betweeclass variace) for all possible threshold values [18]. If there are two classes C 0 ad C 1, so that the Otsu method will maximize the betwee-class variace usig equatio (4) as follows: σ ( ) ( ( )) * = max σ, 1 < L (4) where σ ( ) * is the threshold value of that maximize the betwee-class variace, σ ( ) is the betwee-class variace for each threshold value of, ad is every possible threshold value that exists withi rage 1 ad the maximum value of L i the image. The betwee-class variace itself is computed by equatio (5): 0 ( μ μ ) + ω ( μ ) σ = ω (5) 0 T 1 1 μ T where σ is the betwee-class variace, ω 0 is the probability of class C 0, ω1 is the probability of class C 1, μ0 is the mea of Fig. 7. The example of calculated threshold value (i millimeter) ad the distace of the closest object (i millimeter). The gold colour area i depth image idicates that the area is closer tha the threshold value. I the left picture, the colour of left area of depth image ad the distace value is tured ito red whe the distace is closer tha 1000 mm. 6) Distace Calculatio Fially, the distace of the closest object at each area of depth image is determied by calculatig the average value of depth iformatio that is smaller tha the threshold value. This process is doe by usig equatio (7): i j = = 1 x, i < t where x j is average distace of the closest object at depth image area of j, i is depth value at pixel positio of at depth image area of j, t j is threshold value at depth image area of j, ad is the total of i. This equatio is calculated at each area of depth image, so the result of this process is three distace values, oe for each area of depth image. Fig. 7 shows the example result of calculated threshold value ad distace of the closest object i millimeter. j (7)

5 C. Feedbac Mechaism 4000 mm LEFT AREA MIDDLE AREA RIGHT AREA Visually impaired Voice recommedatio Distace of obstacle Soud otificatio 1 meter 1,5 meter Fig. 8. The system seds voice recommedatio ad soud otificatio to the user oly whe the obstacle reach specific distace from the user. The distace of the closest object obtaied from the autoadaptive thresholdig method is cosidered as a obstacle if the distace is closer tha 1500 mm. The system will give soud beep otificatio every 1.5 secod to the user whe the distace of the closest object is betwee 1000 mm ad 1500 mm. This is based o the data of walig speed of visually impaired, that is, 0.4 m/s if the visually impaired ow that there s a obstacle i frot of him/her [19]. So, if there s a obstacle at distace 1500 mm, there s will be eough time ad space for visually impaired to avoid it before the collisio is happeed. The, whe the distace of the obstacle reaches 1000 mm or closer, the system will give voice recommedatio to the visually impaired usig text-tospeech techology. Fig. 8 depicts whe the system seds soud otificatio ad voice recommedatio to the user. Table 1 shows the voice feedbac that will be set to the visually impaired for each possible coditio. TABLE I. VOICE RECOMMENDATION FOR EACH CONDITION Coditio There s o obstacle i the middle area There s a obstacle i the middle area, but the right area is free There s a obstacle i the middle area, but the left area is free There s o area which free from obstacle Voice Feedbac Go straight Move away to the right Move away to the left Stop IV. EXPERIMENTAL RESULT The experimet has bee coducted to measure (1) the executio time ad () the error that is foud whe comparig the distace obtaied by the system ad the real distace of the object i millimeter. The measuremet is coducted i 9 positios (3 for each area of depth image) as show i Fig. 9. For each positio, there are 6 types of obstacle that will be used, i.e. huma, chair type 1, chair type, trash type 1, trash type, ad walig directio. Table shows the result of the average executio time. As show i that table, the average executio time is 1.4 ms with 4.95 stadard deviatio. This result idicates that the executio time is fast eough to be used i real-time applicatio mm 000 mm 1000 mm 800 mm 0 mm KINECT Left area, 1 meter Left area, meter 3 Left area, 3 meter 4 Middle area, 1 meter 5 Middle area, meter 6 Middle area, 3 meter 7 Right area, 1 meter 8 Right area, meter 9 Right area, 3 meter Fig. 9. The positio of distace measuremet that is used i the experimet. The total positio is 9, each area of depth image get 3 positios. For each positio, there are 6 types of obstacle that will be used. TABLE II. AVERAGE EXECUTION TIME Iformatio Value Average executio time (ms) 1.4 The fastest executiom time (ms) 7 The slowest executio time (ms) 8 Stadard deviatio 4.95 Fig. 10 shows the average error of distace calculatio. As see i the picture, at distace 1- meter, the average error of distace calculatio is less tha 50 mm, but at distace of 3 meter, the average error icreases up to early 300 mm. It is happeed because of two cotributig factors, amely: The pixel accuracy of Kiect s depth image decreases whe the distace betwee the scee ad the sesor icreases, ragig from few millimeters at close rage to about 4 cm at the maximum distace of the sesor [0]. So, basically, the further the distace is, the higher the error will be. The method proposed i this research still could ot differetiate perfectly betwee the object ad the floor, so that at distace further tha 500 mm, both the floor ad the object will be detected. This will decrease the accuracy of the calculatio but it s ot dagerous for the visually impaired because the distace of the object is still far away. Fig. 10. The average error of distace calculatio for each positio of measuremet.

6 The, based o the data i Fig. 10, the average error of the system to calculate the distace of the closest object for all measuremets is mm. This average error is cosidered small for the case of visually impaired because their movemet is imprecise. Beside that, the system will give soud otificatio at distace of 1500 mm. So, if the system fail to give the correct distace measuremet because margi error of 130 mm, the visually impaired still has adequate time ad space to avoid the obstacle. To evaluate the overall system i real-time coditio, iitial trial was coducted with 10 blid-folded persos, with age rage from 0 to 40 years old. The trial was doe i walig corridor at third floor i Departmet of Electrical Egieerig ad Iformatio Techology, Uiversitas Gadjah Mada. Firstly, the participats were iformed about how the system wored. The, they were ased to wal i idoor eviromet from oe poit to aother poit ad followed the istructios from the system. While they were walig, there s a obstacle that bloc their avigatio path, so the system will be tested if it wor correctly or ot. The result of this iitial evaluatio showed promisig outcome. All of blid-folded persos could avoid the obstacle without collide with it. Followig the istructio from the system, 7 persos too the left path to avoid the obstacle whereas the other 3 persos were guided to pass the right path. So, basically, the system successfully guide them although they were directed to pass differet path. For further developmet, we will be tryig to improve the algorithm ad the accuracy of the system i detectig ad calculatig the distace of the obstacle. We ll be addig marer detectio system as well so that the visually impaired ca recogize some iterest poits i idoor eviromet. V. CONCLUSION I this paper, a Assistive Techology (AT) to help visually impaired i avoidig obstacles is developed based o Kiect s depth image techology. A ew approach called auto-adaptive thresholdig is proposed to calculate the distace of the closest object. Auto-adaptive thresholdig searches the most optimal threshold value automatically (auto) ad vary amog each differet area of depth image (adaptive). Based o that threshold value, the distace of the closest object is determied by average fuctio. The system the give soud otificatio ad voice recommedatio whe the obstacle is at distace below tha 1500 mm. The experimetal result shows that the executio time i determiig the closest object is 1.4 ms. It also shows that the average error of the system to calculate the closest object is mm. Moreover, evaluatio with 10 blid-folded persos idicates that the system could successfully guide them to avoid obstacles i real-time coditio. REFERENCES [1] World Health Orgaizatio, Visual impairmet ad blidess, Fact Sheet N 8, 01. [Olie]. Available: [] H. Taizawa, S. Yamaguchi, M. Aoyagi, N. Ezai, ad S. Mizuo, Kiect cae: A assistive system for the visually impaired based o three-dimesioal object recogitio, i IEEE/SICE Iteratioal Symposium o System Itegratio (SII), 01, o. Dec. 01, pp [3] A. Kha, F. Moidee, ad J. Lopez, KiDectect: Kiect Detectig Objects, i ICCHP 1 Proceedigs of the 13th Iteratioal Coferece o Computers Helpig People with Special Needs - Volume Part II, 01, vol. 7383, pp [4] M. H. A. Wahab, A. A. Talib, H. A. Kadir, A. Johari, A. Noraziah, R. M. Side, ad A. A. Mutalib, Smart Cae: Assistive Cae for Visually-impaired People, Iteratioal Joural of Computer Sciece, vol. 8, o. 4, pp. 1 7, 011. [5] M. Oayasu, Newly developed walig apparatus for idetificatio of obstructios by visually impaired people, Joural of Mechaical Sciece ad Techology, vol. 4, o. 6, pp , Ju [6] J. M. Bejami, The Laser Cae, Joural of Rehabilitatio Research & Developmet, vol. BPR 10, pp , [7] D. I. Ahlmar, H. Fredrisso, ad K. Hyyppä, Obstacle Avoidace Usig Haptics ad a Laser Ragefider, i IEEE Worshop o Advaced Robotics ad its Social Impacts (ARSO), 013. [8] E. B. Kaiser ad M. Lawo, Wearable Navigatio System for the Visually Impaired ad Blid People, i IEEE/ACIS 11th Iteratioal Coferece o Computer ad Iformatio Sciece, 01, o. 1, pp [9] S. Koley ad R. Mishra, Voice Operated Outdoor Navigatio System For Visually Impaired Persos, Iteratioal Joural of Egieerig Treds ad Techology, vol. 3, o., pp , 01. [10] V. Kulyui, C. Gharpure, J. Nicholso, ad S. Pavithra, RFID i Robot-Assisted Idoor Navigatio for the Visually Impaired, i IEEE/RSJ Iteratioal Coferece o Itelliget Robots ad Systems (IROS) (IEEE Cat. No.04CH37566), 004, vol., pp [11] S. Ma, J. Huag, R. Jaze, R. Lo, V. Rampersad, A. Che, ad T. Doha, Blid Navigatio With a Wearable Rage Camera ad Vibrotactile Helmet, i Proceedigs of the 19th ACM Iteratioal Coferece o Multimedia - MM 11, 011, p [1] D. Berabei, F. Gaovelli, M. Di Beedetto, M. Dellepiae, ad R. Scopigo, A Low-Cost Time-Critical Obstacle Avoidace System for the Visually Impaired, i Iteratioal Coferece o Idoor Positioig ad Idoor Navigatio (IPIN), Portugal, 011, o. September, pp [13] M. Zöller, S. Huber, H. Jetter, ad H. Reiterer, NAVI A Proof-of- Cocept of a Mobile Navigatioal Aid for Visually Impaired Based o the Microsoft Kiect, i Huma-Computer Iteractio INTERACT 011, 011, o. c, p. pp [14] M. Broc ad P. O. Kristesso, Supportig Blid Navigatio usig Depth Sesig ad Soificatio, i ACM Coferece o Pervasive ad Ubiquitous Computig, 013, pp [15] Microsoft, Kiect for Widows, Huma Iterface Guidelies. 013, pp [16] L. Che, X. Nguye, ad C. Liag, Object segmetatio method usig depth slicig ad regio growig algorithms, i Iteratioal Coferece o 3D Systems ad Applicatios Geeral, Toyo, 010, pp [17] F. A. Jassim ad F. H. Altaai, Hybridizatio of Otsu Method ad Media Filter for Color Image Segmetatio, Iteratioal Joural of Soft Computig ad Egieerig (IJSCE), vol. 3, o., pp , 013. [18] N. Otsu, A Threshold Selectio Method from Gray-Level Histograms, IEEE Trasactios O Systems, Ma, Ad Cyberetics, vol. SMC-9, o. 1, pp. 6 66, [19] A. Rieer ad H. Hartl, Persoal Radar : A Self-govered Support System to Ehace Evirometal Perceptio, i Proceedigs of the 6th Aual BCS Iteractio Specialist Group Coferece o People ad Computers, 01, pp [0] J. Ha, L. Shao, D. Xu, ad J. Shotto, Ehaced computer visio with Microsoft Kiect sesor: a review, IEEE Trasactios o Cyberetics, vol. 43, o. 5, pp , Oct. 013.

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