object classification in traffic scenes using multiple ... · bag-of-words models. under such...
TRANSCRIPT
Object Classification in Traffic Scenes using Multiple Spatio-TemporalFeatures
Guruprasad Somasundaram, Vassilios Morellasand Nikolaos Papanikolopoulos
Deparment of Computer ScienceUniversity of Minnesota
200 Union Street SE, MinneapolisMN 55455
{guru,morellas,npapas}@cs.umn.edu
Saad BedrosHoneywell Labs
1985 Douglas Drive NGolden Valley
Abstract— Object classification is a widely researched areain the field of computer vision. Lately there has been a lot ofattention to appearance based models for representing objects.The most important feature of classifying objects such aspedestrians, vehicles, etc. in traffic scenes is that we have motioninformation available to us. The motion information presentsitself in the form of temporal cues such as velocity and also asspatio-temporal cues such as optical flow, DHOG [6], etc. Wepropose a novel spatio-temporal feature based on covariancedescriptors known as DCOV which captures complementaryinformation to the DHOG feature. We present a combinedclassifier based on properties of tracked objects along with theDHOG and the DCOV features. We show based on experimentson the PETS 2001 dataset and two video sequences of bicycleand pedestrian traffic that the proposed classifier providescompetent performance for distinguishing pedestrians, vehiclesand bicyclists. Our method is also adaptive and benefits fromthe availability of more data for training. The classifier is alsodeveloped with real-time feasibility in mind.
I. INTRODUCTION
Video surveillance is one of the prime motivators for com-puter vision research. Powerful computer vision algorithmshave obviated the requirement for hours of painstaking man-ual examination of traffic footage. The amount of informationthat can be extracted is difficult to quantify and over theyears innovations in computer vision and machine learningresearch have aided us in obtaining more and more infor-mation from traffic videos which would have earlier beendeemed difficult. For automatic extraction of informationabout the traffic count of each object class, we need todevelop powerful object classifiers.The additional challenge involved in developing object clas-sifiers for traffic data is the effect of view we have overthe scene. Most traffic cameras have a bird’s eye viewof the scene and therefore the objects look much smallerthan in many other scenarios. An example of surveillanceunder near and far view conditions is shown in figure 1.Near field is often favored since we obtain more resolutionof the objects and therefore we obtain better classificationperformance. This is particularly true of feature based modelssuch as bag-of-words [13] using interest point detectors likeSIFT (Scale Invariant Feature Transform), SURF (speeded uprobust features) etc. In far field situations there usually is notenough interest point features detected to build meaningful
bag-of-words models. Under such circumstances we resortto low level reliable features. When the resolution of theimages is low, we use a more overall description of theimages using feature descriptors such as the histogram oforiented gradients (HOG) [9], region covariance descriptor[18], etc. In addition to describing the appearance using thesedescriptors we also provide spatio-temporal description ofa sequence of objects using features like DHOG and theproposed DCOV features. These features are simple andefficient to compute. The DHOG as well as the DCOVfeatures can be computed just from the gradient and colorinformation of the extracted blobs. An Intel Quad Coreprocessor (2.4GHz) machine with 4GB of RAM, can processupto 15 frames per second for a video of size 320 × 240pixels.
Formulating a suitable classifier is as important as devisingdescriptive features. Support vector machines (SVM) [8]have been successfully employed for a variety of differentclassification and regression tasks. A thorough discussionof SVM based object classification using different types offeatures is provided in [20]. We can build multiple classifiersbased on different types of features and combine their classi-fication decisions suitably in order to enhance the overall per-formance. Often times the combined classifier outperformsany of the individual classifiers used in the combination. Wepropose a Naive Bayes classifier for combining classificationusing tracked object properties such as blob area, perimeter,eccentricity, solidity along with spatio-temporal features suchas DHOG and DCOV. The combined classifier provides thebest performance overall due to the complementary natureof the individual classifiers.
In the next section we discuss some relevant prior work inliterature dealing with the problem of classifying movingobjects. Then we propose the differential covariance feature(DCOV) based on the Jensen-Bregmann divergence of posi-tive semi-definite matrices in section III. This is followed bya discussion of our method of combining different featuresfor classification. Finally we analyze the performance of ourclassifier on real data obtained from the PETS 2001 datasetas well as two bicyclists and pedestrian traffic videos insection IV and provide concluding remarks in section V.
Fig. 1. The difference between near and far field-of-view surveillance.Objects are much smaller in far field thereby we can obtain much lessappearance information.
II. RELATED WORK
Object classification and detection literature is dominatedby methods that perform classification using single images.These methods aim towards classifying objects under varyingconditions of illumination, scale, orientation, shear, etc. Thebag-of-words model [13] focuses on learning the distributionof stable features that can be reliably matched across varyingconditions to recognize objects. Other models such as theparts-based discriminative object models [11], [10], [14]focus on identifying different parts of an object and un-derstanding the spatial correspondence between them. Theseapproaches are however more suitable to scenarios wherethere are more pixels in the target object. This is the nearfield situation.The far field scenario suffers from fewer pixels on targetthereby rendering the bag-of-words, parts-models, etc. un-reliable. However for our analysis we deal with situationsinvolving static cameras and moving objects. Under suchcircumstances we can perform background modeling andsubtraction to obtain only the moving objects. These fore-ground objects are usually characterized based on simplemorphological features such as blob area, aspect ratio, so-lidity, eccentricity, etc. [4], [3], [16]. These methods are notrobust to tracking artifacts such as blob merging, splittingand inaccurate tracking resulting in wavering blob extraction.These effects cause simple measures such as area, perimeter,etc. to fail and these measures are often not view indepen-dent.A very popular spatio-temporal measure is optical flow [5],[1] estimation. Accurate estimation of optical flow can yielddiscriminating information about the motion of objects. Sincepedestrians move distinctly compared to vehicles, a suitabledescription of the motion is obtained using optical flow. In[19] segmentation of rigid bodies using dense optical flow isdiscussed. Despite many efforts to improve the computationalburden of estimating optical flow, it is still considereddemanding for real-time needs. Also pedestrians are not rigidbodies due to the amorphous nature of the silhouette of thehuman structure. Non-rigid body segmentation is feasibleusing optical flow albeit expensive [15].Recently in [21], [6] methods that use simple appearancebased features which also take advantage of the temporalinformation are discussed. Chen et al. introduced the DHOGfeature based on the changes introduced in the HOG descrip-tor due to rigid and non rigid motion of vehicles and pedes-
trians. They argued that by using the weighted differences ofHOG features discrimination can be obtained. Even thoughHOG features provide good description of objects theyonly capture the spatial distribution of gradients and theirorientations. We propose the use of covariance feature whichcan measure the distribution of multiple features of differenttypes such as color, first order and second order gradients,etc. in one representation. By observing the differences in thecovariance feature (DCOV) in a manner similar to the DHOGfeature we obtain additional information for discriminationbetween different object classes.
III. APPROACH
In this section we first propose the differential covariancedescriptor (DCOV). Then we describe our baseline classifierbased on blob properties and then finally we describe theNaive Bayes combined classifier which is used to enhancethe baseline classifier using the DHOG and DCOV.
A. Differential Covariance Descriptor
The region covariance descriptor was introduced by Tuzelet al. for the purpose of object classification and detection.For any region of interest we can compute different kinds offeatures F at each pixel location in that region of interest. Inthe original paper of Tuzel et al. they propose the followingset of features.
F (x, y) = [x, y, R(x, y), G(x, y), B(x, y)
|∂I(x, y)
∂x|, |∂I(x, y)
∂y|, |∂
2I(x, y)
∂x2|, |∂
2I(x, y)
∂y2|]T
(1)
Here x,y are the pixel locations. R, G and B are red,green and blue color channel values at that location. I isgray scale intensity, and the partial derivatives represent thefirst and second order gradients along the x and y directionsrespectively. We can also add more feature of interest tothis set providing more descriptive power. Given this featureset at each x, y location, we can compute the covariancedescriptor of a particular region as
C =1
n− 1
n∑i=1
(fi − µ)(fi − µ)T (2)
Where fi is the feature descriptor of the ith pixel inthe region of interest. µ is the mean feature of all pixelsin the region. Hence given the 9 dimensional feature F ,the covariance matrix C will be 9 × 9 dimensions. Thiscovariance matrix measures the correlation between relatedfeatures of each pixel in a region. It gives us a way ofcombining different types of feature into one descriptor. Justas the HOG descriptor models the distribution of orientedgradients, the covariance is related to the distribution ofall the features over which it is computed. Traditionallycovariance descriptors are used in nearest neighbor searches.A variety of distance metrics exist for comparing positivesemi-definite matrices in general which can be applied tocovariance matrices.
One such metric is proposed in [12] and described here:
ρ(C1, C2) =
√√√√ n∑i=1
ln2λi(C1, C2) (3)
Where ρ is the distance between two covariance matricesC1 and C2. Here λi are the generalized eigen values ofC1 and C2. The log-euclidean distance metric has beenstudied for comparing diffusion tensors [2]. By taking thematrix logarithm of the covariance matrices and then directlycomparing the upper triangular portion of the matrices withthe euclidean metric gives good matching results and it isalso faster for very large covariance matrices.
A more theoretically correct and fast metric for computingthe distance between covariance matrices was proposed byCherian et al. [7]. This metric is based on the Jensen-Bregmann logdet divergence (JBLD) and it is a dissimilaritymeasure between two positive semi-definite matrices. It isdefined as follows:
J(C1, C2) = log|C1 + C2
2| − 1
2log|C1.C2| (4)
We determined that out of the three metrics, we obtainedbest run times with the JBLD metric. Also theoretical justi-fication as to why this is a more suited metric is provided in[7]. Given the distance metric we can compute the differen-tial covariance (DCOV) feature. We will be dealing with blobimages obtained from tracking. These blobs are typicallyvery small images. For example a pedestrian blob occupiesaround 20× 40 pixels. Since these blobs are tracked acrossmultiple frames, we have information about the distancetraveled by the object between successive frames. For eachframe we can compute the covariance descriptor of the blob:Ci for the ith frame. Then the DCOV feature is computedas follows
dcov =
∑ni=2 J(Ci, Ci−1)× di
D(5)
Where di is the distance traveled by the object betweenframe i − 1 and i, and D is the total distance traveled bythe object during the course of the tracking and n is thetotal number of frames. In comparison the DHOG feature iscomputed as follows:
dhog =
∑ni=1 Θi × di
D(6)
where
Θi = 1−∑j
min(HOGji−1, HOG
ji ) (7)
is the intersection of the two histograms between succes-sive frames i and i − 1 and the superscript j representsthe jth component of the histogram. In both cases thefeatures are weighted based on the distance traveled betweensuccessive frames to counter problems like objects becomingtemporarily stationary.
B. Baseline Moving Object Classifier
Figure 2 describes the general flow of operation in ourclassifier. While processing the traffic video, we update abackground model using the mixtures of gaussians approach[17]. After subtracting the background, we extract the fore-ground objects (blob) and use a Kalman filter on the blobposition and velocity information to perform tracking. Withtracking we obtain information about the blobs such asarea, velocity, blob perimeter, eccentricity etc. Our baselineclassifier is based on the approach in [16] which usesblob properties and velocities to classify pedestrians andbicyclists. However we modify their approach by trainingsupport vector machines on a set of blob features such asarea, convex area, perimeter, eccentricity and solidity. Inaddition we learn a gaussian model for the velocities. Boththe support vector machine and the gaussian pdf of velocitiesestimate a probability for each blob Psvm and Pvelocity as towhich class they belong to. In this framework, we can alsohandle multiple moving objects in a single frame as they aretracked invidually. Then the classification is done as follows:
label = arg maxclass
Psvm(class)× Pvelocity(class) (8)
The class that maximizes the product of the two esti-mated probabilities is the predicted label. This represents ourbaseline classifier. The properties used by this classifier aresimple and fast to compute and can be reliably computedacross varying conditions of resolution, viewpoint etc butthey may not good features to use for classificatio. Howeverfor our test scenarios this classifier performs well.
Fig. 2. A flowchart showing the sequence of processing performed on eachframe of a video sequence.
C. Combined Naive Bayes Classifier
According to the Naive Bayes approach, each classifierestimates a probability of membership of an object to aparticular class based on a feature Xi. Then the individualpredictions can be combined in a Bayesian manner as shown
in Eq. 9. In our case we have the baseline classifier basedon morphological properties and velocity. Then we haveclassification using DHOG and DCOV values. For these twoproperties we again model them individually as gaussiandistributions. With the three classifiers we can obtain acombined probability estimate which maximizes the productin equation 9.
P (class|X1....n) = arg maxclass
n∏i=1
P (class|Xi) (9)
IV. EXPERIMENTS AND RESULTS
We compare and analyze the different representation schemesand their corresponding classifiers on two datasets. The firstdataset is the PETS 2001 dataset. This dataset consists of farfield occurrences of pedestrians and vehicles. This datasetserves primarily as a test bench for evaluating trackingalgorithms. However due to the relatively low resolution ofthe moving objects it presents a challenge for classifyingobjects using appearance based models. The distributions ofarea and perimeter values of pedestrians and vehicles fromone sequence in this dataset are shown in figure 4. Thedistribution of velocities of pedestrians and vehicles from thesame sequence is also shown. We do not use any calibrationinformation. The training data is obtained from the trainingsequences for both cameras in this dataset (dataset 1 to 3).The test sequences are evaluated individually based on themodels obtained from the corresponding training sequences.Then the overall performance is measured by averaging theaccuracies for the individual sequences. The PETS 2001 isa rather small dataset in terms of the volume of the detectedtraffic objects. In each sequence there were only 20 to 30objects out of which a majority were pedestrians. Table IIshows the accuracies of classification of the baseline clas-sifier, DHOG classifier, DCOV classifier and the combinedclassifiers separately. We notice that the combined classifierhas better performance than any of the individual classifiersillustrating the fact that the information provided by each ofthe individual features are complementary to each other. Inother words in situations where area or perimeter may notbe enough to distinguish an object the DHOG value or theDCOV value might be useful. Because we pack in multiplefeatures into the covariance descriptor, the DCOV featureoften tends to be more informative.The second dataset that we evaluated upon consists oftwo traffic video sequences of pedestrians and bicyclists.The reason we use this dataset is that unlike vehicles andpedestrians, bicyclist and pedestrian velocities are not farapart. Also the morphological structure of bicyclists is quitedifferent compared to pedestrians and vehicles. The bicyclistis also a composite mixture of a person and a bicycle, mak-ing the problem of appearance based classification betweenpedestrians and bicyclists non-trivial. In this dataset the firstsequence is obtained from a bicycle trail where pedestriansand bicyclists are more commonly found as individuals.Pedestrians tend to move quicker due to activities such asjogging. Bicyclists also tend to have more speeds than usual.
Class DHOG DCOVPedestrian 0.25 0.28
Vehicle 0.15 0.18Bicyclist 0.4 0.32
TABLE IGENERALLY OBSERVED DHOG AND DCOV VALUES FOR DIFFERENT
CLASSES.
Classifier AccuracyBaseline 90.4%
DHOG Gaussian 89.1%DCOV Gaussian 90.4%
Baseline + DHOG 92.1%Baseline + DCOV 94.2%
Combined 95.7%
TABLE IIPETS 2001 DATASET RESULTS
More intra-class variability in the appearance of bicyclistscan also be found due to the presence of tandems, recliners,etc. The second sequence in this dataset is obtained froma university scenario. In this case pedestrians tend to moveslower and have the tendency to form groups. Bicyclists ridemore carefully thereby moving slower and situations such aswalking a bicycle are not uncommon thereby making thevelocities more ambiguous. Sample frames from the twosequences are shown in figure 6. The area and perimetervalue distribution and the velocity distributions from thetwo sequences are shown in figures 7, 8, 9(a), and 9(b)respectively. By analyzing the DHOG and DCOV values forpedestrians and bicyclists we can learn about the general-izability of these two features to more classes. This datasethas a lot more instances of pedestrians and bicyclists thanwhat the PETS 2001 dataset had. There were about 350bicyclists and 400 pedestrians between the two sequences.We used about 50 each for obtaining training data for theclassifiers. The rest were used for testing. The experimentalresults on the two datasets are illustrated in tables III andIV respectively. Again we notice an improvement in theperformance of the baseline classifier when information fromthe DCOV and DHOG features are added. In one scenariothe DHOG feature performs slightly better than the DCOVand in the other case the trend is reversed. This shows thecomplementary nature of these two features.Another interesting observation was that the DHOG andDCOV values were somewhat consistent across differentscenarios and settings. A general trend for the DHOG andDCOV values are shown in table I. From these resultswe observe that the DCOV and DHOG values can beextended to classify objects such as bicyclists in additionto pedestrians and vehicles. Both these features are simpleand fast to compute and provide competitive performance onchallenging datasets.
Fig. 3. Images from the PETS 2001 Dataset.
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Distribution of velocity values
PedestriansVehicles
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Fig. 4. Probability distribution of area,perimeter and velocity values forpedestrians and vehicles from a sample training sequence of the PETS2001 dataset. Due to fewer samples the gaussian approximations of thedistribution are shown. The velocity gaussian is used to make predictionbased on velocity whereas the area and perimeter are used in a SVMclassifier.
V. CONCLUSIONS
In conclusion we summarize our contributions. We pro-pose a novel spatio-temporal feature called the differentialcovariance feature (DCOV). This feature models the changein appearance of objects as defined multiple types of cuesincluding color when compared to the DHOG values whichmeasures the change in the distribution of gradients only. Wealso show that the DCOV and DHOG are complementaryand can be used in tandem in Naive Bayes setting to obtainimproved classification performance. We validate our claimwith classification experiments on the PETS 2001 (pedes-trians vs. vehicles) dataset and real traffic video data frombicyclist and pedestrian traffic (pedestrians vs. bicyclists).The obtained DCOV values are consistent across differentscenarios and can be used reliably as discriminant for dif-ferent traffic object classes. Since the computed features are
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DCOV distribution
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Fig. 5. Probability distribution of DHOG & DCOV values for pedestriansand vehicles from a sample training sequence of the PETS 2001 dataset.
scalars, simple classifiers suffice to provide discrimination.
VI. ACKNOWLEDGMENTS
This material is based upon work supported in part bythe U.S. Army Research Laboratory and the U.S. ArmyResearch Office under contract #911NF-08-1-0463 (Proposal55111-CI), Honeywell Labs, and the National Science Foun-dation, through grants #IIP-0443945, #IIP-0726109, #CNS-0708344, #CNS- 0821474, #IIP-0934327, and #IIS-1017344.
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TABLE IIIPERFORMANCE ON BICYCLE TRAIL DATA
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Classifier AccuracyBaseline 94.69%
DHOG Gaussian 88.4%DCOV Gaussian 86.59%
Baseline + DHOG 95.12%Baseline + DCOV 95.07%
Combined 96.1%
TABLE IVPERFORMANCE ON UNIVERSITY WALKWAY DATA
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Fig. 10. Probability distribution of DHOG & DCOV values for pedestriansand bicyclists from training data obtained real traffic videos.
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