Adaptive Compressive Tracking Based on Perceptual Hash Algorithm Lei ZHANG, Zheng-guang XIE * and Hong-jun LI

Transcription

1 2017 2nd Inernaional Conference on Informaion Technology and Managemen Engineering (ITME 2017) ISBN: Adapive Compressive Tracking Based on Percepual Hash Algorihm Lei ZHANG, Zheng-guang XIE * and Hong-jun LI School of Elecronic Informaion, Nanong Universiy, Nanong , China *Corresponding auhor Keywords: Compressive Tracking, Percepual Hash Algorihm, Real-ime Performance. Absrac. As he compressive racking algorihm is easily failed o rack arge due o he classifier learning rae is fixed when appearances and lighings of arge ge seriously changed and compleely loses he arge afer heavy occlusion, we propose adapive compressive racking algorihm based on percepual hash algorihm. I makes he compressive racking algorihm more adapive o he change of arge appearances by adjusing he learning rae of classifier in real ime according o he Hamming disance beween he hash fingerprins of curren arge and he original one. Experimenal resuls show ha he mehod can quickly and accuraely rack he arge in he case of he objec appearances change and he objec is occluded. Our racker accuracy and robusness have been improved and real-ime performance. Inroducion In recen years, wih he developmen of compuer video processing echnology, compuer vision has become a ho research opic [1]. As an exremely imporan par of compuer vision, moving objec deecion and racking echnology has been widely sudied and applied o video surveillance, image processing, arificial inelligence and so on[2-3]. Compared o he early arge racking algorihms, such as mean filering, paricle filering, opical flow and emplae maching mehod, scholars have pu forward a lo of effecive arge racking algorihms ha have made a grea breakhrough. However, he objec racking is sill challenging because of he change of arge pose, moion blur, he change of illuminaion and especially heavy occlusion. A presen, he research focus of real-ime arge racking is he discriminaive algorihm [4] ha poses he racking problem as a binary classificaion ask. By choosing he appropriae classifier, i classifies he candidae image blocks and selec he image block wih he maximum confidence as he arge locaion. The arge model classifier updaes laer o improve he racking performance. Grabner e al. [5] proposed an online boosing algorihm o selec feaures for racking. However, i only uses one posiive sample (i.e, he curren racker locaion) a few negaive samples while updaing he classifier. As he appearance model is updaed wih noisy and poenially misaligned examples, his ofen leads o he racking drif problem. Babenko e al. [6] inroduced muliple insance learning ino online racking where samples are considered wihin posiive and negaive bags or ses in order o solve he problem of classifying samples wrong. However, due o he complexiy of calculaion, i can mee he requiremens of real-ime. In [7] an algorihm named online discriminaion feaure selecion is proposed. Hare e al. [8] demonsraed a mehod ha classifies arge and background by using suppor vecor machine algorihm works well in racking. Zhang e al. [9-10] proposed an efficien arge racking algorihm based on compressive sensing heory. I akes he generalized haar-like feaures of arge and background as he posiive and negaive samples of online learning o updae classifier, which grealy reduces he complexiy of racking algorihm and has good real-ime performance. However, while occlusion occurs in racking, all of he above mehods are easy o lose he rue arge by regarding he background as objec, which leads o failure of racking. I is required o design an objec racking mehod o deal wih his problem. 41

2 Proposed Mehod The deficiency of he discriminaive algorihm, such as compressive racking algorihm, is ha he updae rae of classifier fixed. I doesn know wheher or no he curren racking arge is rue. The arge emplae will be incorrecly updaed wih background when he arge is obscured, which leads o drif or loss of arge and racking failure afer long occlusion. In his paper, we combine he compressive racking wih percepual hash algorihm o improve is performance for occlusion. The phash algorihm is mainly used in similar image search. I generaes a fingerprin sring for each image o deermine wheher he images are similar by comparing Hamming disance beween fingerprins. The phash Algorihm procedure is as follows: 1) Resize he dimension of arge image o 32*32; 2) Use discree cosine ransformaion (DCT) on he gray image o ge he DCT coefficien marix wih he size 32*32; 3) Preserve he 8*8 marix of he upper lef corner and calculae he mean value of DCT marix; 4) Se values of he 8*8 marix which are greaer han or equal o he mean value o 1 and ohers o 0. The composior of he marix makes up a 64 i ineger, which is he fingerprin of he image. As long as he overall srucure of he image remains unchanged, he fingerprin is unchanged. The differen bis, i.e. he Hamming disance d beween wo srings shows he similariy degree of wo images. d = 0 means wo images are very similar while d > 10 means he difference of wo images are more han fify percen, which is verified massive experimens. The racking algorihm is able o deermine wheher he curren arge is he arge of ineres and adjus he updae rae λ of classifier real-ime in Eq. 1 o keep he arge emplae correc. λ1, d < 5 λ = λ 2, 5 d < 1 0 1, d 10 The classifier sops updaing while heavy occlusion occurring ( d > 10 ), which makes i possible o locae he real objec afer occlusion, and updaes quickly when arge changes slowly ( d < 5). This mehod effecively improves he accuracy of arge racking wih occlusion. Our algorihm are summarized as follows: Inpu: -h video frame 1) Calculae and save he hash fingerprin H -1 of he arge a he (-1)-h frame. 2) γ Sample a se of image paches, D = { z l( z) l 1 < γ} where l 1 is he racking locaion a he (-1)-h frame, and exrac he feaures wih low dimensionaliy. 3) Use rained classifier o each feaure vecor v( z ) and find he racking locaion l wih he maximal classifier response. 4) Calculae and save he hash fingerprin H of he curren arge. 5) Adjus Hamming disance d beween H -1 and H, and λ as (1). 6) α ζ, β Sample wo ses of image paches D = { z l( z) l < α} and D = { z ζ < l( z) l < β} wih α < ζ < β. 7) Exrac he feaures wih hese wo ses of samples and updae he classifier. Oupu: Tracking locaion l, learning rae λ and classifier parameers. (1) Experimenal Resuls We perform exensive evaluaions on VOT 2014 daase o validae our approach. We compared wih he compressive racking (CT) [9] and he fas compressive racking (FCT) [10] o verify he 42

3 effeciveness and rubusness of he proposed algorihm in racking wih heavy occlusion. The search radius for drawing posiive samples is se o α =4 which generaes 45 posiive samples. The inner and ouer radii for he se X ζ, β ha generaes negaive samples are se o ζ =8 and β =30, respecively. We randomly selec 50 negaive samples from se X ζ, β. The search radius for se D γ o deec he objec locaion is se o γ =20 and abou 1100 samples are generaed. And he learning rae λ 1 is se o 0.35 and λ 2 is se o The compuer is configured o Inel Core i processor, 2GB ram, Win7 32 operaing sysem, and he sofware environmen is MATLAB 2011b. In order o objecively compare he racking performance of hree racking algorihms, he success rae (SR) and cener locaion error (CLE) are used o evaluae he racking resuls. Cener error represens he mean value of he Euclidean disance beween he posiion cener of racking and he cener of he acual arge locaion. The success rae is defined area( ROIT ROIG ) as: score = where ROI T means he racked region of curren frame and area( ROI ROI ) T G ROI G means he acual region. The racking of curren frame is successful while score 0.5. The average resuls of 50 imes simulaion are shown in Table 1. Bold fons indicae he bes performance. I s easy o find ha he performance of our algorihm has improved significanly in racking possible o locae he rue objec afer heavy occlusion according o he resul ha shown in Figure 1. Conclusion In his paper, a simple and efficien mehod based on percepual hash algorihm is proposed for objec racking when occlusion occurs frequenly. The performance of he compressive racking algorihm in racking ha he objec is obscured or los is evidenly improved by adjusing he learning rae of classifier in real ime, which makes he arge emplae more adapive and proper. We have successfully achieved he resuls ha he mehod works quickly and accuraely when arge is occluded or los and is able o locae he real objec afer heavy occlusion. Robusness is significanly improved. Acknowledgmens This work was suppored by he Naional Naural Science Foundaion of China (NO , he Universiy Science Research Projec of Jiangsu Province (NO. 16KJB510036), he Science and Technology Program of Nanong (NO. MS ), Nanong Universiy Undergraduae Training Program for Innovaion (NO ). Table 1. Cener locaion error (in pixels) and success rae(%). Video sequences CLE SR Ours CT FCT Ours CT FCT Coke Coupon David Faceocc Faceocc Subway Suv Sylveser Tiger Walking

4 (a) David (b) Sylveser (c)subway (d) faceocc2 (e) suv (f) walking2 (g) suv (h) walking2 Figure 1. Screenshos of some sampled racking resuls. References [1] Wu Yi, Lim J, Yang Ming-hsuan. Online Objec Tracking, A Benchmark[C]//Conference on Compuer Vision and Paern Recogniion. IEEE, 2013: [2] Hongjun Li, Ching Y. Suen. A novel Non-local means image denoising mehod based on grey heory[j]. Paern Recogniion, 2016, 49(1): [3] Hongjun Li, Ching Y. Suen. Robus face recogniion based on dynamic rank represenaion[j]. Paern Recogniion, 2016, 60(12): [4] Al N, Hinersoisser S, Navab N. Rapid selecion of reliable emplaes for visual racking[c]//conference on Compuer Vision and Paern Recogniion. IEEE, 2010: [5] Grabner H, Grabner M, Bischof H. Real-Time Tracking via On-line Boosing[C]//Briish Machine Vision Conference. 2006: [6] Babenko B, Yang Ming-hsuan, Belongie S. Visual racking wih online Muliple Insance Learning[C]// Conference on Compuer Vision and Paern Recogniion. IEEE, 2009: [7] Zhang Kai-hua, Zhang Lei, Yang Ming-hsuan. Real-ime objec racking via online discriminaive feaure selecion[j]. IEEE Transacions on Image Processing, 2013, 22(12): [8] Hare S, Saffari A, Torr P H S. Sruck: Srucured Oupu Tracking wih Kernels[C]//Inernaional Conference on Compuer Vision. IEEE, 2011:

5 [9] Zhang Kai-hua, Zhang Lei, Yang Ming-hsuan. Real-ime compressive racking[c]//12h European Conference on Compuer Vision. Springer-Verlag, 2012: [10] Zhang Kai-hua, Zhang Lei, Yang Ming-hsuan. Fas Compressive Tracking[J]. IEEE Transacions on Paern Analysis and Machine Inelligence, 2014, 36(10):