Multi-Modality Tracker Aggregation:From Generative to Discriminative

Xiaoqin Zhang1, Wei Li2, Mingyu Fan1, Di Wang1, Xiuzi Ye11College of Mathematics & Information Science, Wenzhou University, Zhejiang, China

2Taobao(China) Software Company Limited, Zhejiang, Chinazhangxiaoqinnan@gmail.com, yichan.lw@taobao.com, {fanmingyu, wangdi, yexiuzi}@wzu.edu.cn

Abstract

Visual tracking is an important research topic incomputer vision community. Although there arenumerous tracking algorithms in the literature, noone performs better than the others under all cir-cumstances, and the best algorithm for a particu-lar dataset may not be known a priori. This mo-tivates a fundamental problem-the necessity of anensemble learning of different tracking algorithmsto overcome their drawbacks and to increase thegeneralization ability. This paper proposes a multi-modality ranking aggregation framework for fusionof multiple tracking algorithms. In our work, eachtracker is viewed as a ‘ranker’ which outputs a ranklist of the candidate image patches based on its ownappearance model in a particular modality. Thenthe proposed algorithm aggregates the rankings ofdifferent rankers to produce a joint ranking. More-over, the level of expertise for each ‘ranker’ basedon the historical ranking results is also effectivelyused in our model. The proposed model not onlyprovides a general framework for fusing multipletracking algorithms on multiple modalities, but alsoprovides a natural way to combine the advantagesof the generative model based trackers and the thediscriminative model based trackers. It does notneed to directly compare the output results obtainedby different trackers, and such a comparison is usu-ally heuristic. Extensive experiments demonstratethe effectiveness of our work.

1 IntroductionVisual tracking is a key component in numerous video anal-ysis applications, such as visual surveillance, vision-basedcontrol, human-computer interfaces, intelligent transporta-tion, and augmented reality. It strives to infer the motionstates of a target, e.g., location, scale, and velocity, from theobservations in the each frame with the assumption that theappearance of the target is temporally consistent. This as-sumption may be occasionally violated due to the large ap-pearance variations of a target induced by changing illumina-tion, viewpoint, pose, occlusion, and cluttered background,

which leaves visual tracking a quite challenging task afterdecades of intensive study.

In terms of how to construct the appearance models, thetracking algorithms can be roughly categorized into genera-tive model or discriminative model based methods. The gen-erative model based methods usually build a model to de-scribe the visual appearance of a target, e.g., by a distribu-tion or subspace of intensities or features. Then the trackingproblem may reduce to search for the optimal motion stateof a target that yields the most similar object appearance ina maximum likelihood formulation. An image patch model[Hager and Belhumeur, 1998], which takes the set of pixelsin the target region as the model representation, is a directway to model the target, but it loses the discriminative infor-mation that is contained in the pixel values. The color his-togram [Comaniciu et al., 2003] provides global statisticalinformation about the target region which is robust to noise,but it is sensitive to illumination changes and distracters withsimilar colors. [Stauffer and Grimson, 1999] first employa Gaussian mixture model (GMM) to represent and recoverthe appearance changes in consecutive frames. Later, a num-ber of more elaborate Gaussian mixture models are proposedfor visual tracking [Jepson et al., 2003; Zhou et al., 2004;Wang et al., 2007]. In [Porikli et al., 2006], the object to betracked is represented by a covariance descriptor which en-ables efficient fusion of different types of features and modal-ities. Another category of appearance models is based on sub-space learning. In [Black and Jepson, 1998], a view-basedeigenbasis representation of the object is learned off-line, andapplied for matching successive views of the object. How-ever, it is very difficult to collect training samples that coverall possible viewing conditions. To deal this problem, sub-space learning based tracking algorithms [Lim et al., 2004;Zhang et al., 2010; 2014] are proposed to effectively learnthe variations of both appearance and illumination in an incre-mental way. Generative models generally seek a compact ob-ject description and do not take advantage of the backgroundinformation. Therefore, the generative model based trackersare apt to be distracted by background regions with similarappearances during tracking.

To address the background distraction problem, discrimi-native models are adopted and trained online during tracking,which concentrate on maximizing the difference between atarget and the background. Hence, visual tracking is viewed

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)

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as a binary classification problem that finds the optimal deci-sion boundary to distinguish a target from the background.[Collins et al., 2005] firstly note the importance of back-ground information for object tracking, and formulate track-ing as a binary classification problem between the tracked ob-ject and its surrounding background. In [Lin et al., 2004], atwo-class FDA (Fisher Discriminant Analysis) based modelis proposed to learn a discriminative subspace to separate theobject from the background. In [Avidan, 2007], an ensembleof online weak classifiers are combined into a strong classi-fier. [Grabner et al., 2006] adopt online boosting to selectdiscriminative local tracking features. [Saffari et al., 2009]introduce a novel on-line random forest algorithm for featureselection that allows for on-line building of decision trees.[Babenko et al., 2011] use multiple instance learning insteadof traditional supervised learning to learn the weak classifiers.This strategy is more robust to the drifting problem. Appar-ently the classification performance of discriminative mod-els largely depends on the correctly labeled training samples.The common choice is to regard the current tracked object asthe positive sample and sample its neighborhood locations toselect negative samples. The self-training nature , that is, theclassification results are directly utilized to update the classi-fier, will lead to overfitting.

Although there are numerous tracking algorithms in the lit-erature, no mater generative based or discriminative based, noone algorithm performs better than the others under all cir-cumstances, and the best algorithm for a particular datasetmay not be known a priori. This motivates a fundamen-tal problem-the necessity of an ensemble learning of differ-ent tracking algorithm to overcome their drawbacks and toincrease the generalization ability. Besides, a natural stepforward to cope with the self-training problem suffered bythe discriminative appearance models is to introduce genera-tive appearance models to supervise the sample selection andtraining of the discriminative model in a co-training man-ner [Yu et al., 2008], since the generative and discrimina-tive models have the complimentary advantages in modelingthe appearance of the object. However, the major challengeof combining different tracking algorithms is how to mea-sure the performance of different trackers in the absence ofgroundtruth? When trackers employ different features frommultiple modalities, it is hard to directly evaluate their per-formance [Wu et al., 2013].

To address the above issues, we propose a multi-modalityranking aggregation framework for fusion of multiple track-ing algorithms. In our work, each tracker is viewed as a‘ranker’ which outputs a rank list of the candidate imagepatches based on its own appearance model in a particularmodality. Then, the proposed algorithm aggregates the rank-ings of different rankers to produce a joint ranking. More-over, the level of expertise for each ‘ranker’ based on the his-torical ranking results (tracking results before current videoframe) is also effectively used in our model. The main fea-tures of our work are two-fold: (1) Our work provides a gen-eral framework for fusing multiple tracking algorithms em-ploying different features from multiple modalities. It doesnot need to directly compare the output results obtained bydifferent trackers, and such a comparison is usually heuristic

and is only feasible in some specific conditions. (2) Our workprovides a natural way to combine the advantages of the gen-erative model based trackers and the the discriminative modelbased trackers. To our best knowledge, such a framework forfusion of multiple tracking algorithms has not been addressedin the literature.

The rest of the paper is organized as follows. In section2, the multi-modality ranking aggregation framework is in-troduced. In section 3, the proposed tracking algorithm isdetailed. The experimental results to validate our method arepresented in section 4. Some concluding remarks are made insection 5.

2 Multi-Modality Ranking AggregationFramework

2.1 Ranking Aggregation ModelIn this part, we will briefly introduce the theory of rankingaggregation. Let O = {o1, o2, .., oN} be a set of object can-didates to be ranked by different rankers, and let ri ∈ SNbe the ranking list of ranker i for these candidates where SNis a pool of all the possible rankings. Assuming that a setof ranking R = {r1, r2, ..., rK} from K individual rankersis available, the probability of assigning a true ranking ξ tothe candidates O can be defined using the extended Mallowsmodel [Lebanon and Lafferty, 2002]:

p(ξ|α,R) =1

Z(ξ, α)p(ξ) exp(

K∑i=1

αid(ξ, ri)) (1)

where p(ξ) is a prior of ξ, and Z(ξ, α) =

Σξ∈SN p(ξ) exp(∑Ki=1 αid(ξ, ri)) is a normalizing con-

stant. d(., .) is the distance measure between two rankinglists, and α = {α1, α2, ..., αK} is the expertise of all therankers which fulfils that αi < 0. The closer of αi to zero,the less influence of the i-th ranker on the assignment ofthe probability. When αi trends to negative infinity, thecorresponding ranker trends to be the true rank. So the Eq.(1)calculates the probability that the true ranking is ξ, given therankings from different rankers R and the degrees of theirexpertise α. In the learning process, only the rankings fromdifferent rankers R are available, based on which we need toinfer ξ and α.

In the above model, the distance d(., .) between two rank-ings need to be right-invariant, which means the value ofd(., .) does not depend on how the objects are indexed. Morespecially, if we re-index the object using τ , the distancebetween two rankings over the objects does not changes:d(ξ, r) = d(ξτ, rτ), where ξτ is defined by ξτ(i) = ξ(τ(i)).That means, the value of d(., .) does not change if we re-index the objects such that one ranking becomes ξξ−1 =e = (1, 2, ..., n) and the other rξ−1. Kendall’s tau distance[Lebanon and Lafferty, 2002] is an example of common right-invariant distance function. The distance between ranking ξand r is defined as the minimum number of pairwise adjacenttranspositions needed to turn one ranking into the other:

d(ξ, r) =N−1∑i=1

∑j>i

I(ξr−1(i)− ξr−1(j)) (2)

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Figure 1: Generative process

[Klementiev et al., 2008] proved that if the distance isright-invariant, the following generative process can be de-rived based on Eq. (2):

p(ξ,R|α) = p(ξ)K∏i=1

p(ri|αi, ξ) (3)

This generative process can be described in Fig.1. From thegenerative model, we can find the observation is ranking ri,ξ and α are the hidden parameters of the model. Based onthe generative model, a set of samples can be sampled. Thesesamples are used to approximate the distribution of the rank-ings.

The parameters α and ξ can be estimated using the EMalgorithm [Klementiev et al., 2008] given a set of rankingsR = {r1, r2, ..., rK}. In the E-step, the true ranking ξ is takenas the missing data. The expected value of the complete datalog-likelihood with respect to missing data ξ, observed dataR, and current expertise estimate α′ is defined as follows:

Q(α;α′) = E[log p(R, ξ|, α)|R,α′] (4)The calculation of the expected value in above equation

results in:Q(α;α′) =

∑ξ∈SN

L(α)U(α′) (5)

where L(α) is

L(α) = log

N∑i=1

p(ξj)−N log

K∑i=1

Z(αi) +

N∑j=1

K∑i=1

αid(ξj , rji )

(6)where rji is the ranking generated by the i-th ranker for the

object candidate j, and U(α′) is

U(α′) =N∑j=1

p(ξj |α′, rj) (7)

where rj is the ranking generated by all rankers for the objectcandidate j.

In the M-step, Eq.(5) is maximized by αi with the follow-ing derivation:

Eαi(d(ξ, ri)) =∑ξ∈SN

(1

N

N∑j=1

d(ξj , rji ))p(ξj |α′, rj) (8)

For the Kendall’s tau distance, Eαi(d(ξ, ri)) is the expec-

tation of Eq.(2) and can be expressed in the following form

[Fligner and Verducci, 1986]:

Eαi(d(ξ, ri)) =

Neαi

1− eαi−

N∑j=1

jeαij

1− eαij(9)

At each iteration, a sampling method is introduced to obtainthe approximate value of the RHS (right-hand side) of Eq.(8). Since Eαi

(d(ξ, ri)) is monotone decreasing, αi can beeasily obtained by a binary search approach.

2.2 Top-k Ranking Aggregation ModelAs we can see, direct application of the above learning pro-cedure is expensive, when the number of the candidates Nis large. In tracking applications, it is reasonable that thegroundtruth will be contained in the top-k candidates. To re-duce to computational complexity, we do not need to do therank aggregation on the whole list of the candidates.

Top-k lists are partial rankings indicating preferences overdifferent (possibly, overlapping) subsets of k objects, wherethe elements not in the list are implicitly ranked below all ofthe list elements. Top-k ranking aggregation model meansthat the ranking aggregation is conducted on the top-k lists ofall rankers. We find the above model can be easily extended tothe top-k rank aggregation, the only difference is the Eαi(.),which can be calculated as follows.Definition 1: Let Fξ and Fri be the top-k elements of ξ andri respectively, with |Fξ| = |Fri | = k. Z = Fξ ∩ Fri with|Z| = z. P = Fξ \ Z, and S = Fri \ Z, with |P | = |S| =k − z = l.Therefore,

Eαi(d(ξ, ri)) =

keαi

1− eαi−

k∑j=l+1

jeαij

1− eαij+l(l + 1)

2

−l(z + 1)eαi(z+1)

1− eαi(z+1)(10)

3 Multi-Modality Ranking Aggregation basedTracking Algorithm

Inspired by the above discussion, we propose a multi-modality ranking aggregation based tracking algorithm. Inour work, we use three generative model based trackers anda discriminative model based tracker. The three generativemodel based trackers are: (1) the covariance matrix basedtracker [Porikli et al., 2006]; (2) the incremental subspacelearning based tracker [Lim et al., 2004]; (3) the spatial-colormixture of Gaussians based tracker [Wang et al., 2007]. Thediscriminative model based tracker is the MIL (multiple in-stance learning) based tracker [Babenko et al., 2011].

3.1 Algorithm DescriptionA flowchart of our algorithm is presented in Fig.2. The par-ticle filtering tracking framework is adopted for all the track-ers. In the prediction process, we first use the state transitionto generate a set of candidate image patches corresponding tothe object states {si}Ni=1. In the tracking process, all the fourtrackers evaluate these candidate image patches according totheir own appearance model, and output a set of ranking lists

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Figure 2: A flowchart of our proposed algorithm

based on their evaluation value. Then the top-k rankings areaggregated using the above model, and obtain the final rank-ing of the candidate image patches. The top-1 candidate im-age patch in the aggregated ranking is considered as the track-ing result. All the four trackers are updated according to theaggregated ranking results respectively. In this way, the MILtracker is supervised by the generative model based trackersand does not trained the classifiers using its own tracking re-sult, therefore the self -training problem is avoided. Also inthe instance selection process, even if the MIL tracker itselffails to locate the true object location during some frames, theother generative model based trackers may still help it selectthe correct positive bag.

3.2 Learning of the Ranker ExpertiseThe above ranking aggregation model obtains the expertiseαi of each ranker ri using the EM algorithm in an iterativeway. Then the true ranking ξ is deduced. It is an unsuper-vised ranking aggregation, and the expertise of each ranker isindependent for different frames. However, it is not reason-able in tracking applications. In fact, the expertise of eachranker should meet the following two requirements: (1) theexpertise of each ranker will vary little between two consecu-tive frames, and this consistency constraint should be consid-ered; (2) the expertise of each ranker should reflect whether aranker is a good tracker which needs to be adaptively evalu-ated in the tracking process.

After obtaining the aggregated ranking ξ, we can evaluatewhether the ranker ri is a good tracker by calculating the dis-tance value d(ri, ξ). However, direct using this distance valuefor evaluation is not appropriate, for example, if ri changesthe top-2 candidates of ξ or the bottom-2 candidates of ξ, thedistance value between them is the same. The former casemeans ri do not give the same tracking results as ξ, while forthe latter case, we think ri give a very good tracking perfor-mance. To simplify this problem, we only focus on the top-1candidate of the rankings. If the top-1 candidate of ri and ξ isthe same, we multiply the expertise αi by a factor m (m > 1)which means ri is reliable. While if the top-1 candidate of riand ξ is not the same, we multiply the expertise αi by a factorm (m < 1). To take the historical ranking results and con-

sistency constraint into consideration, the expertise parametercan be adaptively initialized in each frame as follow.

αti = wαt−1i ∗m+ (1− w)αt−1i (11)

where w = 1 − e−1/σ acts as a forgotten factor and σ is apredefined constant. This warm start learning of the expertiseαi can accelerate the EM convergence process and make theresults more accurate.

3.3 Implementation DetailIn this paper, the object is localized using a rectangular win-dow and its state is st = (tx, ty, w, h) where (tx, ty) denotesthe center location of the bounding box and (w, h) are re-spective the width and height of the bounding box. Someimportant parameters are set as follows: N = 600, k = 90,α0i = −1, m = 1.1 or 0.9, σ = 2.

4 ExperimentIn this section, we test the proposed multi-modality rank-ing aggregation based tracking algorithm (MRAT) on sev-eral challenging sequences. The difficulties of these se-quences lie in that the background is noisy, the object un-dergoes large appearance changes, occlusion and the objectis similar to the background. We compare our algorithmwith five state-of-art algorithms. The first four algorithms arerespectively multiple instance learning based tracker (MIL)[Babenko et al., 2011], the L1-regularized sparse templatebased tracker (LRST) [Mei and Ling, 2011], the visual de-composition tracker (VDT) [Kwon and Lee, 2010], and theonline adaboost tracker (OBT) [Grabner et al., 2006]. We alsochoose co-training multiple instance learning tracker (CMIL)[Lu et al., 2011] as comparison1. In CMIL, it improves theMIL through combining HOG-classifier and RGB-classifierin a co-training way. While we improve the MIL tracker

1The source codes of these trackers are available from:http://vision.ucsd.edu/ bbabenko/project-miltrack.shtmlhttp://www.ist.temple.edu/ hbling/code/http://cv.snu.ac.kr/research/ vtd/http://www.vision.ee.ethz.ch/boostingTrackers/http://ice.dlut.edu.cn/lu/Paper/FG2011/code/COMILcode.rar

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(a) Car

(b) Boat

(c) Small target

(d) Surf

(e) Shake

(f) Football

(g) Woman

(h) Girl

Figure 3: The tracking results of §4.1.(Please refer to the PDF version and enlarge the figure for a clear view of the results)

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through ranking aggregation instance selection, so it is in-teresting to compare these two algorithms. In the trackingresults, MRAT is located using a white bounding box, MILis located using a red bounding box, LRST is located using agreen bounding box , VDT is located using a yellow boundingbox, OBT is located using a green bounding box and CMIL islocated using a light blue bounding box.

4.1 Qualitative AnalysisCar, Boat and SmallTargetThe Car, Boat and Smalltarget sequences are selected to testthe performance of our algorithm against noisy and clutteredbackgrounds. In the Car sequence, a car moves in a verynoisy environment. The nearby background is so noisy thatthe car can not easily be located even by eyes. Some repre-sentative frames of the tracking results are shown in Fig.3(a).MRAT outperforms other trackers and successfully tracks allthe frames. The other trackers fail to locate the object as thebackground becomes noisy.

In the Boat sequence, the surround background of the boatshares similar colors with it. Some representative frames ofthe tracking results are shown in Fig.3(b). MRAT and MIL areable to track the object. The VDT drifts away in some framesand can relocates the object. The other three trackers do notperforms well in this sequence.

In the Smalltarget sequence, the object has a similar ap-pearance to the background and there exists background dis-traction when the pedestrian passes by the car. Some repre-sentative frames of the tracking results are shown in Fig.3(c).MRAT and the VDT tracker can successfully track all theframes. The other trackers can not accurately locate the ob-ject when the background distraction exists.

Surf and ShakeThe Surf and Shake are chosen to test the robustness of ouralgorithm against large appearance, illumination changes andcluttered backgrounds. In the Surf sequence, the surfer has asimilar appearance with the wave and endures large appear-ance changes when he is covered by the wave. Some repre-sentative frames of the tracking results for this sequence areshown in Fig.3(d). MRAT achieves the best performance. Itonly fails to accurately locate the object as the scale of theobject becomes so large that the bounding box cannot includethe object. The other trackers cannot provide accurate results.

In the Shake sequence, the man changes his appearanceby shaking his head. The background is cluttered and alsoundergoes large illumination changes. Some representativeframes of the tracking results are shown in Fig.3(e). The MILtracker successfully tracks all the frames. MRAT only failsto track the object around frame frame 60 and then quicklyrecovers to track the object. The other trackers do not performwell in this challenging sequence.

Football, Woman and GirlThe last three sequences are Football, Woman and Girl. Weapply MRAT on these three sequences to test the robustnessagainst occlusion and background distractions. In the Foot-ball sequence, severe occlusion happens and the target objectshares a similar color with the other players. Some repre-sentative frames of the tracking results for this sequence are

Alg. MIL OBT LRST VDT CMIL MRATCar 26.9 22.6 16.3 10.7 21.9 3.8Boat 4.7 26.6 60.1 8.1 75.6 4.2Surf 28.4 46.5 44.2 17.2 61.6 10.1

Smalltarget 6.2 196.1 7.8 3.5 32.1 3.1Shake 10.3 13.2 52.2 13.0 63.3 11.1

Football 11.7 808 100.1 10.5 41.2 4.4Woman 12.2 78.7 86.5 4.5 110.1 5.2

Girl 24.9 40.3 24.6 14.2 54.3 10.8

Table 1: Quantitative results for the sequences in §4.1

shown in Fig.3(f). The MIL and VDT tracker both begin tolose the object when severe occlusion and background dis-traction happen. MRAT successfully tracks the object in thischallenging situation.

In the Woman sequence, the woman undergoes occlusionby the car. In the Girl sequence, the girl undergoes large ap-pearance changes by turning around her head and is severelyoccluded by the man. Some representative frames of thetracking results for these two sequences are shown in Fig.3(g)and Fig.3(h). Both MRAT and VDT can successfully track theobject through all the frames. The MIL tracker, it can trackthe object for most of frames but without accuracy. While theother trackers do not perform well in these two sequences.

4.2 Quantitative AnalysisIn this part, we present quantitative evaluations to show theperformance of trackers in different video sequences. We cal-culate the RMSE (root mean square error) of the four pointsin the bounding box between the tracking results and thegroundtruth. The groundtruth is marked by hand. The meanof RMSE of the seven sequences we test on are given in Ta-ble 1. From the quantitative evaluations, MRAT provide thelowest RMSE in most of the sequences.

4.3 DiscussionThe qualitative and quantitative results show that MRAT out-performs the other five competing trackers in most of thesequences. MRAT is robust in the presence of the back-ground clutter and distraction, large appearance and illumina-tion changes, and occlusion. It improves on the MIL throughtraining the tracker under the supervision of the generativemodel based trackers in a ranking aggregation manner. Inthe sequences where the MIL successfully tracks the object,the proposed aggregation strategy does not worse the per-formance of MIL tracker. However, in the sequences thatthe MIL fails to track the object, MRAT improves the per-formance of the MIL tracker and can successfully track theobject. As a result, we conclude that MRAT provides an ef-fective solution to the problem of self -training.

5 ConclusionIn this paper, we propose a novel multi-modality ranking ag-gregation tracking algorithm to improve performance of theMIL under the supervision of the generative model. In ourranking aggregation framework, the discriminative classifierbased MIL tracker is boosted with the help of the generative

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models in ranking aggregation manner. The discriminativemodel and generative models are fused together seamlesslywithout the need to compare the performance of each other.The experimental results validate the effectiveness of our al-gorithm.

AcknowledgmentsThis work is supported by NSFC (Grant Nos. 61472285,6141101224, 61473212, 61100147, 61203241 and61305035), Zhejiang Provincial Natural Science Foun-dation (Grants Nos. LY12F03016, LY15F030011 andLQ13F030009), Project of science and technology plans ofZhejiang Province (Grants No. 2014C31062).

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