TRAFFIC CLASSIFICATION AND SPEED ESTIMATION IN TIME SERIES OF AIRBORNE OPTICAL REMOTE SENSING IMAGES

Transcription

1 TRAFFIC CLASSIFICATION AND SPEED ESTIMATION IN TIME SERIES OF AIRBORNE OPTICAL REMOTE SENSING IMAGES G. Palubnskas * and P. Renartz German Aerospace Center DLR, Wesslng, Germany Gntautas.Palubnskas@dlr.de Commsson III - WG III/5 KEY WORDS: Classfcaton, estmaton, modellng, change detecton, sequences, aeral, optcal, magery ABSTRACT In ths paper we propose a new two level traffc parameter estmaton approach based on traffc classfcaton nto three classes: free flow, congested and stopped traffc n mage tme seres of arborne optcal remote sensng data. The proposed method s based on the combnaton of varous technques: change detecton n two mages, mage processng such as bnarzaton and flterng and ncorporaton of a pror nformaton such as road network, nformaton about vehcles and roads and fnally usage of traffc models. The change detecton n two mages wth a short tme lag of several seconds s mplemented usng the multvarate alteraton detecton method resultng n a change mage where the movng vehcles on the roads are hghlghted. Further, mage processng technques are appled to derve the vehcle densty n the bnarzed and denosed change mage. Fnally, ths estmated vehcle densty s related to the vehcle densty, acqured by modellng the traffc flow for a road segment. The model s derved from traffc classfcaton, a pror nformaton about the vehcle szes and road parameters, the road network and the spacng between the vehcles. Then, the modelled vehcle densty s drectly related to the average vehcle speed on the road segment and thus the nformaton about the traffc stuaton can be derved. To confrm our dea and to valdate the method several flght campagns wth the DLR arborne expermental wde angle optcal 3K dgtal camera system operated on a Do-228 arcraft were conducted. Experments are carred out to analyse the performance of the proposed traffc parameter estmaton method for hghways and man streets n the ctes. The estmated speed profles concde qualtatvely and quanttatvely well wth the reference measurements. 1. INTRODUCTION Durng the past years, ncreasng traffc appears to be one of the major problems n urban and sub-urban areas. Traffc congeston and jams are one of the man reasons for mmensely ncreasng transportaton costs due to the wasted tme and extra fuel. Conventonal statonary ground measurement systems such as nductve loops, radar sensors or terrestral cameras are able to delver precse local traffc data wth hgh temporal resoluton, but ther spatal dstrbuton s stll lmted to selected motorways or man roads. A new type of nformaton s needed for a more effcent use of road networks. Remote sensng sensors nstalled on arcrafts or satelltes enable data collecton on a large scale thus allowng wde-area traffc montorng. Synthetc aperture radar (SAR) sensors due to ther all-weather capabltes seem to be well suted for such type of applcatons. Ground movng target ndcaton approaches based on the Dsplaced Phase Center Arrays technque are currently under nvestgaton for arborne SAR sensors and space borne satelltes, e.g. TerraSAR-X, but stll suffer from the low vehcle detecton rate, qute often below 30% (Meyer 2007). Traffc montorng from optcal satelltes s stll lmted due to the not suffcently hgh spatal resoluton, but the detecton of vehcle queues seems to be promsng (Letloff 2006). As t s shown already n (Renartz 2006, Hnz 2008) arborne optcal remote sensng technology has a great potental n traffc montorng applcatons. Several arborne optcal remote sensng systems are already n expermental use at the German Aerospace Center DLR, e.g. arborne 3K camera system, consstng of three dgtal cameras capable of acqurng three mages per second (Kurz 2007), and LUMOS (Ernst 2003). Automatc detecton of vehcles and estmaton of ther speeds n sequences of optcal mages s stll a challenge. Most known approaches are mage based and stll result n a too low completeness (e.g. less than 70%) thus beng not yet sutable e.g. for the estmatng of the traffc densty and flow (Rosenbaum 2008). In ths paper we propose a new model based approach and nvestgate ts potental for the traffc parameter estmaton n congested stuatons n sequences of arborne optcal remote sensng data. Instead of detectng each ndvdual vehcle and then estmatng ts speed (mcroscopc model) as e.g. n (Rosenbaum 2008) we explot a lnear vehcle densty-speed relatonshp for a road segment (macroscopc model) to derve vehcle speeds from the estmated vehcle denstes n an mage. * Correspondng author. 37

2 The paper s organzed as follows: frst, the proposed method s descrbed n Secton 2, then, the results of experments and dscussons are presented n Secton 3, followed by conclusons and references. 2. APPROACH Our approach for traffc parameter estmaton n sequences of optcal mages conssts of a two level method (see Fgure 1). Frst, traffc classfcaton s performed nto three man classes: free flow, congested and stopped traffc. Then, for each traffc class, modellng of traffc flow on the road segments s performed separately allowng the drect dervaton of the requred traffc parameters from the data, such as the vehcle densty and average speed. Further, other traffc nformaton, lke the exstence of congeston, the begnnng and end of congeston, the length of congeston, actual travel tmes, and so on can be easly extracted. The proposed method s based on the combnaton of varous technques: change detecton, mage processng and ncorporaton of a pror nformaton such as road network (NAVTEQ), nformaton about vehcles and roads and fnally traffc models. The change detecton n two mages acqured wth a short tme lag (~ 2 sec) s mplemented usng the Multvarate Alteraton Detecton (MAD) method (Nelsen 2007) resultng n a change mage where the movng vehcles on the roads are hghlghted. Image processng technques can be appled to derve the vehcle densty n the bnarzed and denosed change mage. Ths estmated vehcle densty can be related to the real vehcle densty, acqured by modellng the traffc flow for a road segment. The model s derved from a pror nformaton about the vehcle szes and road parameters, the road network and the spacng between the vehcles (Palubnskas 2009, 2010). 2.1 Traffc classfcaton Traffc classfcaton s performed n order to group vehcles on the road nto three man classes: free flow, congested and stopped traffc. There exst already some approaches for ths task based on a sequence of mages (e.g. Zeller 2009). We dd not found that they are superor to our proposed sngle mage traffc classfcaton whch addtonally allows drect dervaton of approxmate vehcle densty and thus average speed for a road segment. Frst, a colour (RGB) mage s transformed to a gray (ntensty) mage usng YCbCr colour transformaton (YCbCr 2010), whch s beleved to represent better the ntensty of colour mage then a smple mean. Then, the mage s shfted n along road drecton by a half of vehcle sze and a dfference of orgnal ntensty mage and ts shfted verson s calculated (the roads are straghtened before usng NAVTEQ road database). Fnally, a sum of absolute dfferences (SAD) s calculated for a road segment of a specfed length. Smple thresholdng technque can be appled to classfy SAD values nto three classes: low values correspond to free flow traffc, mddle values - congested traffc and hgh values - stopped traffc. Addtonally, bnarzed and denosed SAD mage can be used to estmate roughly vehcle densty d c for a gven road segment. 2.2 Traffc models For each traffc class a dfferent traffc model s appled n order to derve desred traffc parameters Congested traffc Numerous nvestgatons (e.g. Greensfeld, 1935; Kockelmann, 1998) on real traffc data show that under congested condtons the followng assumpton s true: a class of vehcle s spacng s a lnear functon of the speed of all vehcles S B g( v) L, (1) where spacng S s the front-to-front vehcle dstance n meter, B s a dmensonless parameter of the model, functon g(v) transforms speed (km/h) nto meters, e.g. g(100 km/h) = 100 m, L s the vehcle length n meter and s the vehcle class, e.g. passenger car or truck. Parameter B can be nterpreted n the followng way: for B=0.5 and L=0 the formula (1) means for all drvers a well-known rule of thumb safe dstance = half speedometer readng n metres. As already mentoned ths model well descrbes a congested traffc. The B value s rangng normally between 0.5 and 1.0 (Palubnskas 2009). Now the traffc densty can be calculated as 1km D(# vehcles per km), (2) S where S p S, p s a proporton of vehcle class and p 1. Thus for densty calculaton n (2) the weghted mean value of vehcle spacng s used. The modelled vehcle densty D s drectly related to the average vehcle speed on the road segment and thus the nformaton about the traffc stuaton can be derved (see Fgure 2). Detaled descrpton of the proposed method s provded n (Palubnskas 2009) Stopped traffc For stopped vehcles, the prevously ntroduced way of estmatng vehcle densty s no more vald: the values are sgnfcantly underestmated or even approachng zero due to the subtracton of vehcle blobs n a change mage. Of course, the same model can be appled f vehcle densty d c estmated durng the traffc classfcaton level s used and thus a speed of very slowly movng vehcles can be estmated Free flow traffc For free flow traffc a very smple model s used: that s a maxmal allowed speed for a partcular road segment s assumed thus agan leadng to a rough speed estmate. 3. EXPERIMENTS To confrm our dea and to valdate the method several flght campagns wth the DLR arborne expermental wde angle optcal 3K dgtal camera system operated on a Do-228 arcraft were performed. In ths paper one of these experments s presented. Snce the area covered s qute large, the evaluaton s performed for many road segments and can therefore be regarded as representatve measures. 38

3 3.1 DLR 3K camera system The 3K camera system ( 3 Kopf = 3 head ) conssts of three non-metrc off-the-shelf cameras (Canon EOS 1Ds Mark II, 16 MPxel). The cameras are arranged n a mount wth one camera lookng n nadr drecton and two n oblque sdeward drecton, whch leads to an ncreased FOV of max 110 / 31 n across track/flght drecton. The camera system s coupled to a GPS/IMU navgaton system, whch enables the drect georeferencng of the 3K optcal mages. Based on the use of 50 mm Canon lenses, the pxel sze at a flght heght of 1000 m above ground s approxmately 15 cm and the mage swath s 2.8 km. The pxel sze ncreases up to 50 cm and the swath wdth up to 8 km for a flght heght of 3 km. For more detals see (Kurz 2007). 3.2 Test ste and data The motorway A8 south of Munch s one of the busest parts of the German motorway network wth an average traffc volume of around vehcles per day. The test ste was a 16 km motorway secton between motorway junctons Hofoldng and Weyarn. On 2 nd Sep. 2006, heavy traffc was expected at ths secton caused by homebound travellers n the drecton of Munch. Three 3K data takes were acqured between 14:01 and 15:11 from 2000m above ground n three over flghts. Durng each over flght, 22 mage bursts were acqured each contanng four consecutve mages. The tme dfference wthn these bursts was 0.7 s, so that each car was montored at least for 2.1 s. To collect the reference data each lane was manually processed, that means all vehcles were detected n the mages by vsual nterpretaton and ther speeds measured. 3.3 Traffc parameter extracton Results of traffc parameter extracton on the test ste are shown n Fgures 3 and 4. In Fgure 3 an overvew of results are presented for the mosac of fve frames: hghway secton of approxmately 4 km length. We see that the estmated speed profles concde qute well wth the reference measurements. In Fgure 4 speed estmaton results are presented for each of fve segments separately for more detals. Traffc congeston s defned usually usng the average speed or the traffc densty. Unfortunately, there s no unque defnton because of dfferent types of roads (hghway, cty streets) and moreover t s usually country dependent. Havng the average vehcle speed for each road segment the congeston detecton s a trval task and can be performed by a smple threshold. For example, f the congeston s defned for speed up to 50 km/h (for hghways), then the red coloured areas n Fgures 3 and 4 can be nterpreted as congested ones. Quanttatve evaluaton of speed profles s presented n (Palubnskas 2009). 3.4 Dscussons The performance of the proposed method s very dependent on the good qualty of the geo-referencng of overlappng mages and the qualty of the road data base. A pror nformaton concernng vehcle and road parameters should be adapted very carefully to the regonal traffc condtons. For the accurate vehcle densty estmaton the tme lag between the two mage acqustons should be selected accordng to the constrants presented n (Palubnskas 2010). Image based methods (mcroscopc model) perform normally better for a hgher resoluton (less than 30 cm pxel spacng (Rosenbaum 2008)), thus the arcraft flght heght should be low or equvalently one should take nto account the reduced mage swath. It seems that the proposed model based method s not very senstve to the resoluton because t s workng on the macroscopc model level. We would lke to menton that possble false alarms may occur durng traffc classfcaton due to object shadows on the road (e.g. tree shadows as can be seen n Fgure 4(d)). Because of the usage of only sngle mages for traffc classfcaton the followng objects: objects (not vehcles) on or over the road (brdges, sgns, constructons) or objects nearby the road (tree and house shadows on the roads) cannot be surely separated from vehcles. Further research s needed to ncorporate more nformaton (e.g. GIS, radar sensor data) n ths level. What concerns future work further experments are planned to test the approach for off-nadr scenes and n the ctes durng dfferent envronmental condtons. Another research drecton s amng at dervng other traffc parameters such as traffc densty and traffc flow. 4. CONCLUSIONS A new traffc classfcaton and model-based congeston detecton approach for mage tme seres acqured by the arborne optcal 3K camera system s ntroduced. It allows us to derve one of the man traffc parameters - the average speed - and the vehcle densty as an ntermedate product. Other parameters such as the begnnng and end of congeston, length of congeston and travel tmes can be derved easly from these results. The method s based on the vehcle detecton on road segments by change detecton of two mages wth a short tme lag, usage of a pror nformaton and smple traffc models. Expermental results show the great potental of the proposed method for the extracton of traffc parameters on hghways n along-track scenes. The estmated speed profles concde qualtatvely and quanttatvely qute well wth the reference measurements. 5. ACKNOWLEGMENT The authors would lke to thank our colleagues Franz Kurz, Erch Bogner and Rolf Stätter for ther efforts n plannng the flght campagn, data acquston and data processng. Specal thanks to our tranee Mantas Palubnskas for the mplementaton and valdaton of the traffc classfcaton. 6. REFERENCES Ernst, I., Sujew, S., Thessenhusen, K.U., Hetscher, M., Raßmann, S. and Ruhé, M., LUMOS - arborne traffc montorng system. In: Proceedngs of 6th IEEE Internatonal Conference on Intellgent Transportaton Systems, Shangha, Chna, Greensfeld, B.D., A study of traffc capacty. In: Hghway Research Board Proceedngs, vol. 14, 448:

4 Hnz, S., Lenhart, D. and Letloff, J., Traffc extracton and charactersaton from optcal remote sensng data. The Photogrammetrc Record, 23(124), pp Kockelman, K.M., Changes n the Flow-Densty Relaton due to Envronmental, Vehcle, and Drver Characterstcs. Transportaton Research Record, 1644: Kurz, F., Müller, R., Stephan, M., Renartz, P. and Schroeder, M., Calbraton of a wde-angle dgtal camera system for near real tme scenaros. In: Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, vol. XXXVI-1 (Part W51), Hannover, Germany, 2007, 6 pages. Letloff, J., Hnz., S. and Stlla, U., Detecton of vehcle queues n Quckbrd magery of cty areas. Photogrammtre Fernerkundung Geonformaton, 4, pp Meyer, F., Hnz, S., Müller, R., Palubnskas, G., Laux, C. and Runge, H., Towards traffc montorng wth TerraSAR-X. Canadan Journal of Remote Sensng, 33(1), pp Nelsen, A.A., The Regularzed Iteratvely Reweghted MAD Method for Change Detecton n Mult- and Hyperspectral Data. IEEE Transactons on Image Processng, 16(2), pp Palubnskas, G., Kurz, F. and Renartz, P., Traffc congeston parameter estmaton n tme seres of arborne optcal remote sensng mages. In: Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, vol. XXXVIII (Part W5), Hannover, Germany, 2009, 6 pages. Palubnskas, G., Kurz, F. and Renartz, P., Model based traffc congeston detecton n optcal remote sensng magery. European Transport Research Revew, Onlne Frst Artcles: Renartz, P., Lachase, M., Schmeer, E., Krauß, T. and Runge, H., Traffc Montorng wth Seral Images from Arborne Cameras. ISPRS Journal of Photogrammetry and Remote Sensng, 61(3-4), pp Rosenbaum, D., Kurz, F., Thomas, U., Sur, S. and Renartz, P., Towards automatc near real-tme traffc montorng wth an arborne wde angle camera system. European Transport Research Revew, 1(1), YCbCr color space, accessed on 25 February Zeller, K., Hnz, S., Rosenbaum, D., Letloff, J. and Renartz, P., Traffc montorng wthout sngle car detecton from optcal arborne mages. In: Internatonal Archves of Photogrammetry, Remote Sensng and Spatal Informaton Scences, vol. XXXVIII (Part W5), Hannover, Germany, 2009, 4 pages. Input: one mage Traffc classfcaton Output: classes, d c Class? Free flow Congested (movng) Congested (stopped) Constant speed Output: v=v max Input: two mages Vehcle densty estmaton d Traffc model d=f(v) Output: v Input: d c Traffc model d=f(v) Output: v Fgure 1. Two level concept for traffc classfcaton and model-based speed estmaton. Gray boxes mark nput data for the followng processng modules (blue boxes) and fnally green boxes stand for output parameters. 40

5 Detecton of vehcles on road segment Estmated vehcle densty d Apror nformaton Model Inverson Average speed per road segment v Traffc model d=f(v) Fgure 2. Flow dagram of the proposed traffc congeston detecton method. (a) (b) 0 km/h Fluent flow Fgure 3. Overvew of traffc congeston detecton on A8 hghway between Munch and Salzburg for 3K sensor data acqured durng ADAC flght campagn on 2 nd September Orgnal mosac of fve frames s overlad by speed profles for separate road drectons: (a) reference measurements and (b) estmated values (red colour stands for congested area and green - for free flow traffc). 41

6 b c d e f (a) 1 b (b) c (c) d (d) 2 e (e) f (f) 3 0 km/h Fluent flow Fgure 4. Detaled example of traffc congeston detecton on A8 hghway between Munch and Salzburg for 3K sensor data acqured durng ADAC flght campagn on 2 nd September 2006: (a) Google map wth blue boxes showng the 5 acqured frames (from left to rght) and (b-f) for each frame speed profles for separate road drectons plotted on the orgnal 3K mage (red colour stands for congested area and green - free flow traffc). 4 42