Robust Inlier Feature Tracking Method for Multiple Pedestrian Tracking

Transcription

1 2011 Internatonal Conference on Informaton and Intellgent Computng IPCSIT vol.18 (2011) (2011) IACSIT Press, Sngapore Robust Inler Feature Trackng Method for Multple Pedestran Trackng Young-Chul Lm a* and Chung-Hee Lee a a Daegu Gyeongbuk Insttute of Scence & Technology, Room 511, 5th floor, 3rd research center, 223, Sang-r, Hyeonpung-myeon, Dalseong-gun, Daegu, Republc of Korea nnolyc@dgst.ac.kr Abstract. Ths paper proposes a multple-pedestran-trackng method that uses robust nler features. Our system conssts of pedestran detecton, data assocaton, a multple track management method, and partcle trackng. Ths study prmarly focuses on robust moton predcton algorthms for partcle trackng. The results of the predcton are used for a moton model of a partcle flter framework and the assgnment cost of data assocaton. Robust feature-based moton trackng algorthms are proposed as a means for predctng the target poston. It s mportant to extract dstngushed features and remove outler features to estmate the regon of nterest (ROI) n an accurate and relable manner. Moton predcton should be relable n order to assocate the observaton-to-track pars correctly and update the ROI accurately n a partcle flter framework. The results of ths study show that our moton trackng algorthm estmates the ROI more accurately than prevous methods. Keywords: Pedestran trackng, feature trackng, partcle flter. 1. Introducton Pedestran detecton and trackng problems are consdered one of the most challengng problems n computer vson research. Pedestrans take dfferent poses, appearances, and shapes, and may assume dfferent appearances and poses over tme. Moreover, cluttered envronments and sgnfcant llumnaton changes further complcate such problems. Pedestran detecton s dffcult because few machne learnng technques use explct models; rather, most learnng algorthms learn from examples n mplct models [1]. The frst step for pedestran detecton s to extract the robust features that can accurately dscrmnate between pedestrans. In [2], Haar-lke features are used to detect pedestrans usng the Adaboost-tranng method. DalAl & Trggs [3] show that hstogram of gradent (HOG) descrptors provde better performance than the wavelet [4] or SIFT [5]. However, no stateof-the art detecton algorthms can detect all pedestrans wthout some false detecton. Thus, false negatve and postve errors are trade-offs n the detecton algorthm. Robust and relable trackng algorthms are requred to mnmze both types of errors smultaneously. Generatve and dscrmnatve approaches are wdely used n current trackng methods. The generatve trackng method estmates the target poston usng the maxmum a posteror probablty as searchng specfc regons wth pror nformaton [6]. Snce the onlne classfer estmates the target poston n the dscrmnatve approach, trackng problems are consdered classfcaton problems [7]. Both methods focus on a sngle target trackng problem: the target s ntally set manually. Ths makes t dffcult to apply ths approach to practcal felds such as survellance or ntellgent vehcles. Multple target trackng presents addtonal problems that are not relevant n sngle target trackng, such as data assocaton, automatc track ntalzaton, and termnaton problems. If the multple target trackng algorthms malfuncton, false postve and false negatve alarms wll ncrease durng some mage sequences, 146

2 and track denttes may be changed due to ncorrect observaton-to-track assgnments. In [8], they propose a stereo vson based trackng method to detect and track multple vehcles. Ths approach uses ntegrated poston trackng and a moton trackng method to mnmze false and mssng detectons and to assgn multple tracks to multple detectons robustly. In [9], they propose a mono vson based trackng method to detect and track multple pedestrans usng a detector-confdence-partcle flter and an onlne classfer wth local features. The method used n [8] depends on the poston-trackng results obtaned through stereo vson, whle moton trackng s only used to recover the ROI n case mssng detectons occur. In [9], the weght of the samples of the partcle framework s determned by the detecton, data assocaton, and onlne classfer results. However, they uses a constant velocty model for the moton model of a partcle flter, whch provdes poor predcton results and erroneous data assocaton results n multple pedestran trackng problems. Ths paper proposes a robust nler feature trackng method to predct varous pedestran motons. The method s used for an accurate moton model of a partcle flter and robust data assocaton. Ths paper wll assume the followng structure. Secton 2 provdes an overvew of the system; Secton 3 descrbes the proposed robust nler feature trackng method; Secton 4 presents the results of ths study and analyzes benchmark-test-mage sequences; and Secton 5 presents conclusons and opportuntes for future research. 2. System Overvew Our system conssts of pedestran detector, moton predcton, data assocaton, ROI update usng partcle flterng, and multple track management as shown n Fgure. 1. Fgure 1. System overvew. A. Pedestran DETECTOR Ths paper employs the wdely used HOG pedestran detector [2]. The HOG method presents local hstograms of gradent orentatons n a dense grd. In practce, the mage s dvded nto small cells, and 1- dmensonal (1-D) hstograms of gradent drectons accumulate on the cell. The cell s accumulated and normalzed by a local hstogram over the block, whch has a few cells. The normalzed descrptor blocks represent the HOG descrptors, and the lnear support vector machne (SVM) allows the classfer to learn the descrptors that correspond to the pedestran. In the detecton step, the classfer fnds pedestrans usng a sldng wndow method by searchng all the mage regons. B. Partcle Trackng Ths study uses a partcle flter framework to track multple pedestrans. The partcle flter estmates the state of the target s jont posteror densty usng a samplng method at tme k [10]. p( X k Z k ) p( zk xk ) p( xk xk 1 ) p( X k 1 Z k 1 ), (1) where X k = {x 0, x 1, x k } and Z k = {z 0, z 1, z k }. Accordng to the equaton (1), the jont posteror densty s proportonal to lkelhood, transton densty, and prevous jont posteror densty. To estmate the target poston accurately, t s mportant to approxmate both transton densty of the moton model and the lkelhood of the observaton model. The moton model s partcularly mportant durng multple target trackng because the moton model s used to assgns multple tracks to multple observatons n the data assocaton algorthms and s used for recoverng mssng detectons n the trackng procedure. Ths study uses HOG detector results as the observaton model and proposes an nler feature trackng method for the 147

3 moton model. The proposed method wll predct a partcle flter framework as welll as estmatee the target s ROI for mssed detectons. C. Multple Track Management A multple track management algorthm s requred to remove false detectons and recover mssng detectons. Prevous work [9] ntalzes and termnates a track usng a naïve method such as a few consecutvee assocated detectons and consecutve mssng detectons, respectvely. Our method s based on an event-drvenn track management method that uses a few of track states such as IDLE, PRE-TRACK, CUR- TRACK, and POST-TRACK. The track state transton s occurred by the event whch s trggered by the track score [11], as shown n Fgure 2. In contrast to our prevous Fgure 2. Track state transtons. Fgure 3. Robust nler feature trackng. work [11], the assocated pedestran detecton results and the moton predcton results usng robust nler features calculated the track score. Furthermore, the ntalzed track termnates n the CUR-TRACK statess and termnated tracks can transt to a CUR-TRACK state wthout the ntalzaton procedure necessary n the POST-TRACK states. If there are no assocated detectons durng a perod, the track state enters an IDLE state. 3. Robust Inler Feature Trackng Many researchers have worked for robust feature extracton and feature-based trackng methods. The Harrs corner based-kanade-lucas-tomos (KLT) tracker [12] s wdely used for ts smplcty and fast speed. However, the KLT tracker s based on the sum of the squared dfference of ntensty. The method produces hghly ambguous results when the method search correspondng features, and the method s not robust aganst llumnaton changes. Robust features wth gradent descrptors were used to fnd the correspondng feature ponts [5, 13]. However, these methods dd not remove the outler features. The outler features get the ROI to be drfted whle trackng the target. In [8], the feature cost s calculated n order to select the relablee feature pars usng the normalzed cross correlaton (NCC) cost, kernel cost, and moton cost. However, the method has lmtatons when fndng a target s feature pars where the background s extremely cluttered, there s excessve llumnaton, or the poses change. Our method conssts of Harrs corner extracton, KLT tracker, feature matchng, center moton estmaton, outler feature rejecton, and homography estmaton usng nler features (Fgure 3). Frstly, the Harrs corner detector extracts the canddate features and the KLT tracker estmates the dsplacements of the features. However, there may be some nosy feature pars. Therefore, the wrong pared features are removed by usng the matchng cost, whch s calculated by the NCCC and HOG descrptors. c D E (, j) = c D D p In equaton (2), and D j denote the descrptor vectors of the current th and prevous j th nterest ponts. We need to estmate the homography matrx usng only the nler feature pars to estmate the sze and poston of the ROI. In most cases, the outlers moton vector s dfferent from the nlers moton vector. Therefore, we calculate the mean and standard devaton of the target s moton vector usng the ROI s center regon. The approach s reasonable because t sn t lkely to contan the outler features n the regons. And then, we recalculate the mean and standard devaton n the whole regon of the ROI whle removng the p j 2 (2) 148

4 features, whch are outsde certan ranges. The procedures are terated untl the standard devatons converge. The nler features estmate the homography matrx, whch should occur wth mnmum errors usng n Hˆ = arg mn Hˆ k = 1 2 ( Hˆ ' x x ) + ( Hˆ 1 ' xˆ x ) ' where n s the number of selected feature pars; x and x represent the features postons for prevous and current frames. Fnally, we dscrmnate the nlers from the outlers to estmate the correct transformaton matrx usng the least-medum of squared (LMS) estmaton method. k k 2 (3). (a) Fgure 4. (b) Test datasets : (a) TUD-campus, (b) ETHZ-LOEWENPLATZ, black boxes denotes annotated ground truth.. (a) 149

5 Fgure 5. (b) Pedestran detecton and trackng results : (a) detecton result, (b) trackng result TABLE I. COMPARISON WITH OTHER TRACKING METHODS IN TERMS OF THE ERROR NUMBER THAT THE TRACKER DOESN T RECOVER THE ROI Dataset Frame number KLT Tracker [12] SF-KLT Tracker [8] SURF Tracker [13] Proposed method TUD-campus ETHZ- LOEWENPLATZ Expermental Results Ths study evaluates the proposed nler feature trackng method, both quanttatvely and qualtatvely. We compare our method wth the KLT tracker [12], selected feature-based KLT [8], and SURF tracker [13]. TUD-campus and ETHZ-LOEWENPLATZ sequences are used for quanttatve evaluaton n these experments. After a pedestran s selected manually, the tracker estmates the ROI of the target pedestran (Fgure 4). If a tracker fals to estmate the poston of the target, the number of errors s counted and the ROI s rentalzed by the ground truth. Table 1. shows the expermental results for some pedestrans n the sequences. The expermental results show that the proposed method outperforms the other methods. Fgure 4 shows the HOG detecton results and mult-pedestran trackng results. Our trackng algorthm mnmzes the false and mssng detectons due to the robust nler feature-trackng algorthm. There s stll a number of mssng detectons and track dentty swtchng errors; therefore, we must enhance the pedestran-detecton relablty and data assocaton algorthm. 5. Conclusons Ths paper proposes a robust nler-feature trackng method to predct the target s poston accurately. The proposed method s used for a moton model n our system s partcle flter. The quanttatve evaluatons show that our method outperforms the prevous methods n terms of the number of errors that estmate the target s ROI ncorrectly. Moreover, wth the robust nler-feature trackng method, our mult-pedestran trackng method can mnmze the number of mssng and false detectons and enhance the precson of ROI. However, a few mssng and false detectons reman, as well some track dentty swtchng. We wll mprove our trackng performance whle usng the color nformaton and target specfc appearance model. 6. Acknowledgement 150

6 Ths work was supported by the DGIST R&D Program of the Mnstry of Educaton, Scence and Technology of Korea (11-IT-01 & 10-BD-0201). 7. References [1] Enzwler, M. and Gavrla, D. M., Monocular pedestran detecton: survey and experments, IEEE Trans. Pattern Anal. Mach. Intell. 31(12), (2009). [2] Vola, P., Jones, M. J., and Snow, D., Detectng pedestrans usng patterns of moton and appearance, Proc. of Int. Conf. on Computer Vson, 1, (2003). [3] Dalal, N. and Trggs, B., Hstogram of orented gradents for human detecton, Proc. of Computer Vson and Pattern Recognton, 1, (2005). [4] Mohan, A., Papageorgou, C., and Poggo, T., Example-based object detecton n mages by components, IEEE Trans. Pattern Anal. Mach. Intell., 23(4), (2001). [5] Lowe, D. G., Dstnctve mage features from scale-nvarant keyponts, Int. J. Comput. Vs. 60(2), (2004). [6] Kwon, J. and Lee, K. M., Vsual trackng decomposton, Proc. of Computer Vson and Pattern Recognton, 1, (2010). [7] Babenko, B., Yang, M. H. and Belonge, S., Vsual trackng wth onlne multple nstance learnng, Proc. of Computer Vson and Pattern Recognton, 1, (2009). [8] Lm, Y.-C., Lee, Mh., Lee, C.-H., Kwon, S., and Lee, J.-H., Integrated poston and moton trackng method for onlne mult-vehcle trackng-by-detecton, Optcal Engneerng, 50(07), (2011). [9] Bretensten, M. D., Rechln, F., Lebe, B., Koller-Meer, E., and Gool, L. van, Robust trackng-by-detecton usng a detector confdence partcle flter, Proc. of Int. Conf. on Computer Vson, (2009).. [10] Arulampalam, M.S., Maskell, S., Gordon, N., and Clapp, T. A., Tutoral on partcle flters for onlne nonlnear/non-gaussan Bayesan trackng, IEEE Trans. Sgnal Processng, 50(2), (2002). [11] Lm, Y.-C., Lee, C.-H., Kwon, S., and Km, Jh., Event-Drven Track Management Method for Robust Mult- Vehcle Trackng, n Proc. of Intellgent Vehcles Symp ( 2011). [12] Janbo, S. and Tomas, C., Good features to track, Proc. of Computer Vson and Pattern Recognton, (1994). [13] Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V., "SURF: Speeded Up Robust Features", Computer Vson and Image Understandng, 110(3), (2008). 151