Incremental Detection of Text on Road Signs

Transcription

1 Incremental Detection o Tet on Roa Signs Wen Wu Xilin hen Jie Yang arch 9, 4 U-S-4-6 School o omputer Science arnegie ellon Universit ittsburgh, A 5 Abstract This paper presents a ramework or incremental etection o tet rom roa signs. The approach eicientl incorporates tracking an etection mechanisms into the same ramework. The propose approach irst ins a set o iscriminative eature points an clusters them into ierent regions. We then select caniate sign planes b a combination o color an vertical plane moels. Within etecte roa sign planes, the ramework selects caniate tet regions again base on eature points. The eature points serve a ual purpose: corresponence or tracking i tet has been etecte in the region an cues o caniate regions or tet etection. The ramework urther veriies caniate tet regions using more sophisticate eatures. Once a tet region is conirme, the tracking algorithm will continuousl track the region. The tet region grows as more tet aroun it is etecte rom rame to rame. Eperimental results have emonstrate the easibilit o the propose ramework in incrementall etecting tet on roa signs over the time rom vieo sequences capture rom a moving vehicle. This research was partiall supporte b the U/-RL.

2 Kewors: incremental etection, tet etection, traic sign, vertical plane moel, sign tracking, tet tracking

3 . Introuction Automatic unerstaning o roa signs is an essential task or autonomous an intelligent vehicles. It coul help to keep a river aware o the traic situation b highlighting an recoring signs that have been passe. The sstem coul also rea out the tet on roa signs with a snthesize voice, which is especiall useul or rivers with weak visual acuit. The previous research on roa sign etection an recognition is limite to smbol recognition [7, ]. Researchers evelope sstems or etecting an recognizing smbols, such as stop an curve, etc. These sstems were base on two ierent approaches: segmentation through color thresholing, region etection an shape analsis; segmentation through the borer etection in a black an white image an their analsis. In shape-base recognition, it seems that Gavrila s methos [, ] are superior to other approaches or the implementation o a real-time application. Other methos o roa sign etection inclue color etection [7], color then shape [6, 9], simulate annealing [] an neural networks []. In this paper, we are intereste in automaticall locating roa signs rom vieo input an etecting tet on roa signs. Unlike the previous research, we are intereste in not onl recognizing shapes o roa signs but also unerstaning tet on roa signs. Figure shows our eamples o roa signs. Obviousl, we ace man challenges in etecting tet rom these roa signs as in other obect recognition tasks: Lighting conitions are uncontrollable an changeable because o time an weather variations. Backgroun an oregroun are ver comple. Tet on roa signs varies in ont, size an color. Vieo images are low resolution an nois. Figure Eamples o roa signs In orer to aress these challenges, we propose a robust an reliable ramework that can automaticall an incrementall etect tet on the roa signs in vieo. Vieo sequence contains a large amount o temporal reunant inormation o motion o the camera an obects in the scene. We can take avantage o the reunant inormation an incrementall etect the tet rom rame to rame. Dierent rom most eisting sign etection approaches, b emploing the cues rom the tracking scheme, the propose ramework integrates etection into tracking mechanism. First, the ramework ins a set o iscriminative eatures or current vieo rame. These points will serve as the input or

4 the secon step as well as tracking eatures. Net, the ramework uses two criteria to etect the possible caniate roa sign planes rom the set o eature points. The two criteria are color inormation an vertical plane moel. The irst criterion was wiel use in tet etection algorithms, particularl in the roa sign tet etection. The vertical plane moel is base on the act that D geometric relationship can be recovere rom D ata. Ater this step, the sstem has a set o possible caniate roa sign areas. This ramework applies an ege-base tet etection algorithm to these caniate sign areas. The selecte eatures in these areas provie cues o caniate tet regions or urther etection. The ramework urther veriies caniate tet regions using multi-scale eges. Once a tet region is conirme, the tracking algorithm will continuousl track the region. The ramework repeats this process, an locates roa signs an etects tet on these signs over the time. We have perorme etensive eperiments. Eperimental results inicate that the propose approach can incrementall etect tet on roa signs rom vieo sequences capture rom a vieo camera mounte on a moving vehicle. The rest o this paper is organize as ollows: Section escribes the new ramework in etail. Section iscusses the vertical plane moel. Section 4 introuces sstem implementation an eperimental results. Section 5 gives the conclusion an uture work.. Incremental Tet Detection Inormation retrieval has activate research in automatic etection an recognition o tet rom vieo vieo OR [, 4, 8,,, ]. Tet in vieo can be classiie into two categories: graphic tet an scene tet. Graphic tet is ae to the vieo ater the vieo is capture b visual recoring evice. Scene tet eists in a natural environment, an is irectl capture b a vieo camera. That is, scene tet is part o obects on which it appears. Eamples o scene tet inclue roa signs, avertisement boar, irection signs, tet on costume an consumables. Some earl research problems inclue tet etection rom general backgrouns, an recognition o tet on particular obects like containers an license plates. In recent ears, growing attention has been ocuse on sign tet etection an translation rom photograph an vieo. The earl work mainl ocuse on the prototpe ieas an require human interaction to select the sign area in the image. Recent research attempts have move towar automatic sign etection an recognition [4, 8]. In this research, we are intereste in automatic etection o tet on roa signs rom live vieos. This is a scene tet etection task. Area-base an ege-base methos have been wiel use or etecting tet in an image. Area base metho aims to analze certain eatures in an area, such as teture an color [6]. Some transorms, such as Discrete osine Transorm DT, Gabor iltering, an Gaussian iltering, are use or area analsis. Although these area-base methos have ierent avantages, the share a common problem, i.e., the sensitivit to lighting an scale variations. Ege base metho mainl relies on ege eatures that are relativel more stable to the above problem. Some noise iltering schemes shoul be applie to avoi aing etra eges. This metho has been oun more suitable or tet etection rom natural scenes [4, 8,, 4 ]. Li et. al [] presente an approach etecting tet rom vieo b etecting tets perioicall while tracking in the rest o the perio. The approach use a hbri wavelet/neural network classiier to segment tet regions in the vieo rames. Once the tet is etecte, a multi-resolution sum o square ierences base tracking metho is

5 applie to track the etecte tet. However, tracking an etection were separate moules in the sstem, an the were not integrate to acilitate each other over the time. In orer to eicientl etect tet on roa signs, we propose to combine tracking an etection mechanisms into a same ramework. The attractiveness o this ramework is that it can ull utilize the temporal inormation, an incrementall etects tet on roa sign planes rom rame to rame. In orer to catch human attentions, roa signs are esigne with the ollowing properties: Tet on roa signs is esigne with high contrast to its backgroun. ost roa traic signs appear on vertical planes. Foregroun/backgroun colors o a roa sign are not ranoml istribute. The are istinguishable rom the surrouning environment. The basic iea o the propose approach is to eectivel use these constraints an eicientl integrates the etection mechanism into the tracking process. As illustrate in Figure, the propose ramework consists o ive steps. Figure Incremental etection low at time t k

6 Algorithm. Select eatures. For the current vieo rame, goo eatures in non-tet areas are selecte using some iscriminative criteria the whole image i non-tet area is empt. These eatures will be use in the ollowing steps an some o them will also be tracke;. Fin roa sign plane caniates. Use two criteria to etect possible caniates o roa sign planes rom the cues o eatures oun in the current rame an tracke eatures rom previous rames. The two criteria are color inormation an vertical plane propert o roa signs;. Buil pramial multi-scale images. The constructe multi-scale images o the current rame will serve as input or the ollowing incremental tet etection; 4. Incrementall etect tet. Both new caniates an previousl etecte sign planes will be eamine b an ege-base tet etection metho on the pramial multiresolution images. Detection results rom ierent levels will be combine to etecte tet regions o original scale. We can obtain the etection results b merging matche tet regions. 5. Feeback etection results. The etecte tet regions an eatures within them will be tracke. Their inormation will be taken into account in the analsis o the net iteration. Rea net rame, an go back to Step. The new algorithm works in an iterative manner that enables the algorithm to etect tet incrementall over the time rom a vieo sequence. In the propose ramework, Step & attempt to locate roa signs rom a vieo rame. Step & 4 etect new tet regions in caniates o roa signs an combine them with previous etecte tet regions. Step 5 eebacks current most complete partial etection results to the analsis o net rame. It also tracks the etecte tet regions, sign planes an eatures within them when the net rame comes. Over the time, tet on roa sign planes will be etecte an tracke incrementall until the roa sign planes isappear rom the scene. We escribe the ramework in more etail below, ecept that the vertical plane moel will be introuce in Section. Step : The ke iea o this ramework is to embe tet etection into a tracking process. Robust tracking requires goo eature points. Accurate etection o tet also requires goo eatures. The number o eatures is ecie empiricall to balance the etection rate an computation eicienc. An eample o eature selection will be shown in Section 4. Step : Net, obtaine eature points are clustere b using their coorinates as eatures. olor segmentation in certain color spaces is then perorme to get initial clusters o eature points. The vertical plane moel will be use to etract possible roa sign planes rom the initial eature clusters. Step : ramial construction o an image is wiel use in man tracking an tet etection algorithms. It is a bottom-up process that buil L level image rom L- level image. The original rame image is consiere as level image. This multi-scale approach attempts to solve the problem o ierent sizes o tet. The small tet can be etecte at the lower levels while the large one is eeme to be backgroun. On the other han, the large tets can be oun at higher levels while the small ones will be overlooke. B this strateg, the algorithm can etect tet with ierent sizes b combining the etection results rom ierent pramial levels o the image.

7 Step 4: The algorithm uses ege-base tet etection moule that consists o coarse etection an structure analsis. We use a ierence o Gaussian ege etector to obtain the ege set. We then compute size, intensit, mean, an variance o the ege set within the surrouning rectangle. Using some eature criteria, we can remove some ege patches rom the set, an the rest remains or urther consieration. Net, merge aoining ege patches with similar properties an upate properties o combine ege patches. Since tets in the same contet share common color properties, we can use them to analze the structure o the tet, an urther reine etection results. In this work, a Gaussian iture oel G is use to moel color istributions o the oregroun an backgroun o each region. g c βg µ, θ + β G µ, θ, β. b where G, G are the color istributions o the oregroun an backgroun respectivel. β inicates the compleit o the tet, µ µ b shows the contrast or a color space invariant to the lighting conition, an θ, θb provies the inormation o the tet ont stle. So, each tet can be represente with β, µ, µ b, θ, θb. Ater new tet regions are etecte in the current rame, the will be merge along the string irection with etecte tet regions rom the previous rames. Further, the new obtaine tet region will be tracke. Ol tet regions are remove rom the tracking list i the have been merge. Step 5: The corners o all etecte tet regions an sign planes are tracke b the eature tracker over the vieo sequence. However, tracking perormance is aecte b the problem that the corresponing eatures ma rit. Thus, some constrains are use to reect outlier matche points. a The velocit optical low o each eatures attempts to be consistent with those o its neighbor eatures; b Neighboring eatures shoul sta close to maintain the spatial cohesion, but collision shoul be avoie. Distance an brightness change criteria can also be consiere to reect outlier matches. Ater appling above constrains, we in that the tracking moule works ver well on real vieos ecept when there are suen lightness variations, severe obstruction beore roa signs or isappearance o the signs. Alternativel, we can appl the epipolar constraint to reect outlier matches. The epipolar constraint states that i, are the coorinates o a same spatial point in the real worl in the two rames, the must satis the ollowing equation T F, where F is the unamental matri that represents the epipolar geometr between two images. This equation means that point must pass through the epipolar line eine b F in the secon rame image an vice versa.. A Vertical lane oel..oel ormulation In Step o the ramework, we obtain clusters o eature points using color moels. In this section, we will select caniate roa sign planes b veriing whether those eaturepoint clusters satis the vertical plane moel. The goal o this step is to provie caniate sign regions or incremental tet etection. Another beneit o this strateg is b b b

8 that it urther narrows own the search space o tet etection thus greatl improves the eicienc o the ramework. The basic iea is that most roa sign planes eist as vertical planes in the real worl. Since we can obtain spatial corresponence o eature points in two aacent rames rom the tracking algorithm, we can recover the normal o the caniate planes. Using the vertical propert o sign planes, we can ilter out non-sign planes rom the eature- point clusters. In our approach, we nee, at least, eature points to veri a caniate. Tracking algorithm provies the spatial inormation o ever eature over the time. We then choose three eature points that are not in one line to check i their constructing plane is a vertical plane in D worl or not. We use the ollowing eample to illustrate the iea, Figure shows two aacent vieo rames F, F an their associate two camera coorinate sstems at times t, t. amera ocal length is, an camera moves uring the intermeiate perio. amera coorinate sstem at t, O X Y Z, is the basic coorinate sstem. It uses the vectors, an as its ais. Feature points,, are represente in the basic coorinate sstem, :,, z, :,, z an :,, z o, o are image coorinate sstems at times t, t. As t t is ver small or a real-time vieo sequence, we assume that the vehicle moves along the camera optical ais O Z at t,t. Figure. The basic geometr between two snaps.

9 The proection that maps a point,, : z rom camera coorinate sstem onto two points,, : an, : in the two image coorinate sstems at times, t t is as ollows / / z z. / / z z. Base on above equations. an., we can obtain the ollowing estimation o coorinates. / / / z. Similarl, we can get estimation o an coorinates. Let that k k k k.4 k k k k.5 where,...,, k. In orer to obtain the normal o a caniate plane, we can in the representations o vectors A r an B r as:, : z z A.6 : z z B..7 Then, we can obtain the normal o the caniate plane as

10 Z Y X B A N.8 where X.9 Y. Z. In orer to veri whether the plane o,, is on a vertical plane in the D worl, the equations.9. can be use to recover the normal o the constructing plane o an three points,, rom a eature-point cluster. We then measure the ratio o the X component to the length o vector N. J N X,...,,, /. where J is the number o recovere normal vectors rom one eature-point cluster. A proper averaging scheme can be applie to all achieve normal vectors to minimize iniviual errors as shown in equation.. J....oel Sensitivit Analsis Equations.8-. inicate that the accurac o the normal o the veriie plane highl epens on the accurac o calibrate ocal length. From the equations.9., we can erive Z Y X X N X + +,.4 where Z Y X,, are secon components o.9 -. respectivel. From the above equation, we know that is the unction o the camera ocal length, an the perturbation in prouce b perturbations in is : Z Y X Z X.5 then we can obtain

11 + + X Y Z Z..6 From equation.6 we can observe that, the accurac o normal o the veriie plane linear epens on the accurac o the calibrate ocal length. 4. Sstem an Eperiments We have conucte etensive eperiments to valiate our ramework on real vieo streams o natural scenes. This section provies the etails o the sstem implementation, conucte eperiments, an results. 4..Technical escription o the prototpe sstem The prototpe sstem is implemente an evaluate on a with Intel entium 4 GHz an G memor running Winows X. The evaluation vieo was capture rom a SONY igital vieo camera mounte on a minivan. Intrinsic parameters o the camera were calibrate using the metho propose b Zhang []. The vieo rame size is 64*48. Signs are esigne or human to see easil at a istance. In most o cases, roa signs have ollowing properties:. Tet is esigne with high contrast to its backgroun color.. Tet on the same roa sign has almost the same oregroun an backgroun patterns. These properties enable some points corners on sign planes coul also be goo tracking eatures. Thus, the eature selection is implemente using the algorithm in [8]. Better an more suitable tet etection on roa signs coul be stuie an evaluate. Shi- Tomasi algorithm shows goo perormance in this work. The more number o selecte eatures, the more accurate is the later etection algorithm, while the more computation power is neee. We trie ierent numbers o eatures in our sstem, an 5 were oun to airl balance the etection rate an computation eicienc. To enable the sstem to etect new appeare roa sign planes over the time, new eatures are selecte in non-tet regions an ae to the sstem. oreover, combine with reecting outlier matches strateg mentione in section, upating goo eatures rame b rame alleviates the eature tracker riting problem an improves the robustness an accurac o the tracker. Faster eature selection an tracking optimization [9] can be applie urther to improve the running requenc o the sstem. In orer to etract roa sign planes, obtaine eature points are clustere b using their coorinates in the image as eatures. olor segmentation in certain color spaces is then perorme to get initial clusters o eature points. In this research, we convert the camera RGB color space to the HSI color space, an normalize HSI within the range o [, 55]. The vertical plane moel will be then use to etract possible roa sign planes rom the initial eature clusters. Details o the incremental etection o tet have been introuce in section. Here we will mention a little more about tet structure analsis. Figure 4 shows three situations in which partial tet has been etecte. Since English wors are in horizontal irection in most cases, the tet structure is analze horizontall. In the irst situation, i two tet

12 regions with similar height have the similar roo vertical position, the will be merge to one tet region. Secon case shows that two tet regions with ierent height, while the same roo position, the will also be merge an the new height is ecie b the previous big size region. However, or the thir case, no merging action is perorme because the let region can be better ocuse i it is separate with the right two regions. In the Step o the algorithm, the prami epth is set to be. That means, the highest level image is one ourth o the original image size. Figure 4. Tet Structure Analsis A pramial implementation o the Lucas Kanae Feature Tracker [] is use to track the etecte tet areas. The pramial images come rom Step. The search winow o the optical low is *. Other trackers can also be plugge into the propose ramework easil. 4.. Eperimental Results We have evaluate the propose ramework through eperiments on our traic sign vieo atabase. The atabase consists o hours o various signs vieos, incluing highwa signs, roawa signs, an other tpes o signs. We use an eample o a highwa sign to show the whole etection process in Figure 6 an the etection results o some other signs in Figure 7. Tables & will summarize the etection results o ierent tpes o roa signs uner ierent conitions. Feature selection in the irst step was base on a iscriminative criterion [8]. ost corner eatures can be etracte rom the rame, incluing ones on the roa sign planes. We cluster eature points into regions an use color moels to ilter out some non-sign regions. In orer to reuce risk o removing real sign regions rom this step, we set a ver low threshol. This step can ilter out maorit o non-sign regions. We urther veri the remaining regions using vertical plane moels. A combination o color an geometric

13 inormation can o a ver goo ob to reuce alse etection. Figure 5 shows an eample o tet etection with/without preprocess. With the ilters, the sstem coul etect tet regions correctl Figure 5a. The sstem, however, alsel etecte the rames as tet in the case o no preprocessing Figure 5b. a b Figure 5. An eample o tet etection with/without preprocessing Figure 6 illustrates the process o incremental etection o tet on a roa sign. During the initial ew rames o the vieo, no eatures points are oun on the roa sign planes Figure 6a. On the rame o Figure 6b, some eature points appeare on the roa sign. Net, the sstem classiie the region as a possible roa sign plane an marke with ellow bounar on the image Figure 6c. In the net ew rames Figure 6, partial tets were etecte on the roa sign plane rame b rame. Some partial etecte tet regions were merge base on structure analsis, as shown in Figure 6e. Figure 6 shows that all etecte tet regions are tracke over the time. Finall, all tets on the roa sign are correctl etecte Figure 6g. A emo vieo sequence o Figure 6 has been attache to this submission. a Feature selection b Features on sign c Fin sign plane Initial tet etection e Incremental tet etection Track etecte tet g Incrementall etect all tets Figure 6. An illustration o incremental tet etection

14 In the evaluation process, we notice that, as shown in Figure 6 a-, there was a etour sign in the right sie o the image. This etour sign ha not been etecte b our sstem. The reason was that the color o tet on the etour sign was close to the backgroun o the sign. Since no eature points appeare on the sign, sstem simpl ignore it. This shows the epenenc propert o the propose ramework. Each step o the algorithm contributes to the inal etection result. Increasing the number o eature points can potentiall make the sstem in this etour sign region. But tet etection ma still have problems because even a human ha iicult etecting the tet on the sign rom the vieo, when we evaluate it. To solve this problem, the sstem nees a better vieo camera. Figure 7 shows more etection results rom our roa sign vieo atabase. Goo recall an precision can be observe rom these results. We evaluate the prototpe sstem using several vieo sequences rom our roa sign atabase capture rom a moving vehicle. The sequences were sample at 5 rames per secon. The vieos are categorize base on ierent lightness conitions, e.g., sunn, clou an usk. Table shows the roa sign etection perormance. It is shown that perormance is goo in sunn an clou while perormance is poor in the usk. Table shows the overall tet etection perormance. Figure 7 ore etection results. Table. Roa sign etection perormance Sunn lou Dusk Total # o roa signs in vieo Detecte 7 4 Table Tet etection perormance Sunn lou Dusk Total # o tet regions Full etecte 7 5 artiall etecte Automatic etection o tet on roa signs is a challenging real problem. an issues are associate with the problem, such as the perormance impact o the ierent parts o the sstem, poor perormance in low light conitions, sensitivit analsis o the algorithm perormance, an comparison with other etection algorithms, etc. Our views on the above questions are as ollows. First, goo tet etection rate relies on the qualit o

15 eatures selecte in the irst step an the robustness o the vertical plane moel applie in the secon step o the ramework. An unetecte eample was shown in Figure 6 to illustrate the epenenc relationship. Secon, we are working on a challenging problem an coul not solve all the issues in one paper. We will evelop new algorithms to aress this problem in the uture. The new algorithms, again, can be easil plugge into the propose ramework. Thir, the sstem we built in't require an manual setting o threshols rom vieo to vieo. All the threshols are preset rom the training ata. The perormance is relativel stable to ierent vieo streams uner a reasonable resolution. Lastl, with regar to the comparison with other publishe tet etection algorithms, we are presenting a new ramework to solve a ierent problem, so we are not aware o an other algorithms that can solve the eactl same problem. 5. onclusions Large amounts o inormation are embee in natural scenes. Signs are goo eamples o obects in natural environments that have rich inormation content. Detection o tet on roa signs has man applications in human computer interaction an robotics. Yet it poses new challenges to computer vision communit. In this paper, we have propose a new ramework or incrementall etecting tet on roa signs. The propose ramework makes two maor contributions. First, the ramework eicientl embes tracking an etection mechanisms into the same ramework. Dierent eature selection methos, tracking mechanism, tet etection approaches can be easil plugge into the ramework. We have evelope a prototpe sstem to emonstrate the concept. Secon, the ramework has provie a novel wa to integrate tet etection rom color, D vertical plane etection, an teture cues into tracking scheme. The novelt o the propose work lies in the concept o incremental etection ramework to etect tet rom vieo stream. In act, we can plug in ierent technologies into this ramework. In this paper, in orer to emonstrate the easibilit o the new ramework, we have embee some eicient an eective eisting technologies into it. We have no intention to claim contributions in etension o these algorithms or combinations o them. Eperiments an evaluations have inicate the easibilit an reliabilit o the ramework. The images use in the eperiments consist o most highwa images. However, in some situations sign etection is relativel more iicult as the signs are not clearl visible ue to various reasons. For eample, in more comple situations, the vertical plane constraint oes not hol e.g., twiste sign planes or occlusion makes the sign etection iicult. We aim to solve these interesting problems in the uture work. 6. Reerences []. Betke an N.. akris, Fast obect recognition in nois images using simulate annealing, roc. o IV, pp.5-5, 995. [] D. hen, H. Bourlar, J.-. Thiran, Tet ientiication in comple backgroun using SV, roc. o VR, Vol., pp. 6-66,. [] X. hen, J. Yang, J. Zhang, A. Waibel, Automatic etection o signs with aine transormation, roc. o WAV, pp. -6. [4] Y. heng, ean shit, oe Seeking, an lustering, IEEE Trans. on AI, 78, pp , 995

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