Camera sensor networks have developed as a new technology for the wide-area video surveillance. In view of the limited power andcomputational capability of the camera nodes, the paper presents an abnormal behavior detection approach which is convenientand available for camera sensor networks. Trajectory analysis and anomaly modeling are carried out by single-node processing,whereas anomaly detection is performed by multinode voting. The main contributions of the proposed method are summarizedas follows. First, target trajectories are reconstructed and represented as symbol sequences. Second, the sequences are taken intoaccount using Markov model for building the transition probability matrix which can be used to automatically analyze abnormalbehavior. Third, the final decision of anomaly detection is made through the majority voting of local results of individual cameranodes. Experimental results show that the proposed method can effectively estimate typical abnormal behaviors in real scenes.
1. Introduction
Camera sensor networks consist of low-power microcameranodes, which integrate the image sensor, embedded proces-sor, and wireless transceiver. Multiple camera nodes withdifferent views can provide comprehensive information andenhance the reliability of the captured events. Due to theadvantages of enlarging surveillance area and solving targetocclusion, camera sensor networks are best suited for real-time visual surveillance applications [1, 2]. One of essentialpurposes of visual surveillance is to detect moving targetsand identify abnormal behaviors. In the past, model-basedapproaches have been proposed to tackle the anomaly detec-tion problem. The work in [3] adopted dynamic Bayesiannetworks to model normal activities. An activity will beidentified as abnormal if the likelihood of being generatedby normal models is less than a threshold. Neverthelessan appropriate threshold is hard to determine in practice.In [4], a hidden Markov model (HMM) was applied torepresent normal activities and perform anomaly detection.Note that it is difficult to label all the activities because of thetremendous variety of movement states. Trajectory modelingcan determine the movement anomaly in video sequences,
and many previous works have discussed the issue. In [5],vision-based trajectory learning and analysis methods werediscussed. In [6], a sparse reconstruction analysis of targettrajectory was introduced to detect abnormal behaviors.However, most of these works employ supervised learning torecognize normal behaviors, which requires a large numberof labeled training data. Furthermore, the existing worksmainly focus on maximizing the detection accuracy anddetect abnormal behaviors through a single visual camerawithout involving the information fusion and interactionof camera nodes. These methods are inapplicable to sensornetworks because of the node’s limited power, memory, andcomputational capabilities.
Several approaches using sensor networks have beenproposed to detect abnormal behavior. In [7], the authorspresented an approach with a low false alarm rate to detectabnormal activities by deploying wireless wearable sensors.But the proposed approach adopts some scalar sensorsinstead of camera sensors and its main challenge is thatit is difficult to clearly define abnormal behaviors due tothe uncertainty in the different types of signals receivedfrom these wearable sensors. The work in [8] proposeda sequential syntactical classification approach to detect
Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 839045, 9 pageshttp://dx.doi.org/10.1155/2014/839045
2 International Journal of Distributed Sensor Networks
abnormal behaviors in a network of clustered cameras, wherethe temporal sequence of the camera sensorwith the best fieldof view (FOV) was collected and classified in a centralizedoperations center. Though the approach is able to classifyabnormal sequences correctly, anomaly detection is basedon centralized processing and does not fully consider theresource restrictions of camera nodes. In [9], a framework ofnetworked low cost embedded video sensors was presented,where visual cues, such as projection histogram and ellipticalfitting, were compared with reference patterns to identifyposture and deduce behavior. The drawback of this work liesin the fact that information interaction and collaborationbetween sensors are not explicitly addressed. The workin [10] proposed a HMM-based approach for detectingcrowd behavior by using a heterogeneous sensors networkcomprising visual cameras and a thermal infrared camera.In [11], the authors used HMM and Bayesian network tomodel crowd behaviors and compared the two methods bydetecting abnormal behaviors, fight and robbery, in the scene.Nevertheless the two approaches merely use the featuresextracted from heterogeneous camera sensors to model nor-mal behavior, which is executed by a centralized supervisedlearning method. It is infeasible in distributed camera sensornetworks.
Considering the resource constraints on camera nodes,abnormal behavior detection in camera sensor networksshould be implemented by distributed processing and col-laboration among camera sensors.The distributed processingmeans that the anomaly detection can be run in the local cam-era node, which can significantly reduce the communicationcost of information exchange. Collaboration among cam-era sensors can effectively improve the detection reliability.However, for abnormal behaviors detection in camera sensornetworks, there have been relatively few works concerningthe above issues. As is well known, trajectory analysis is aneffective method to detect abnormal behaviors. In this paper,we propose a novel approach to abnormal behavior detectionbased on trajectory analysis. The proposed method retainslow computation complexity whilst having desirable detec-tion reliability. Each camera node detects moving targetswithin its FOV and extracts target blobs. A target trajectoryconsists of the center positions of a target blob.Then the targettrajectory is reconstructed and treated as a series of stringsthrough symbolic representation. This process reduces thenumber of data to be processed and makes it possible forMarkov model to build transition probability matrix withoutany prior information in complex scenarios. The anomalydetection can be implemented by estimating the anomalyprobability of the input sequence. The final decision is madebased on the majority voting of camera nodes which canimprove the reliability.The experimental results demonstratethat ourmethod is reliable and feasible for abnormal behaviordetection in camera sensor networks.
The remainder of the paper is organized as follows. Sec-tion 2 introduces the moving target detection and the sym-bolic representation of target trajectory. Section 3 describesthe scheme for abnormal behavior detection, node commu-nication, and voting mechanism. Experimental results andanalysis on real video sequences frommultiple camera nodes
are demonstrated in Section 4. Finally, Section 5 concludesthe paper.
2. Trajectory Analysis
2.1. Trajectory Extraction. The results of moving target detec-tion and tracking should be obtained before analyzing trajec-tories. Our earlier work presented an effective backgroundmodeling and update method based on adaptive Gaussianmixture model to extract the target blobs [12]. In the lightof the pixel-by-pixel processing of background modelingbeing computationally expensive, we will further simplifyand improve the method so that it is applicable to cameranodes. First, a video frame is divided into image blockswith a predefined size, 3 × 3 pixels. Second, the averagecolor value of every block is calculated and the image blockis replaced by a pixel with the average value. Thus a newimage frame consisting of these pixels can be constructedand its size is reduced to one-ninth of the original frame.Then, background modeling is implemented on the newimage frame. Obviously, this process can greatly decreasethe computational complexity and eliminate the influenceof noise to a certain degree. Note that the detection resultswill get worse as the size of image blocks increases. Once apixel is classified as foreground in the reconstructed frame,its corresponding image block in the original frame is labeledas target blob. Following target detection, a morphologicalprocess is run to remove artifacts and fill the holes withintarget blobs.
The target position is defined as the center of mini-mum boundary rectangle of target blob. Target trajectoryis composed of a sequence of target positions; thus it canbe extracted by target blob tracking. If there are multiplemoving targets in FOV of a certain camera node, the sametarget blob in different frames can be identified by thehue histogram matching of target blob [13]. Hue histogramis robust to illumination changes and can be constructedusing the hue values of the pixels corresponding to targetblob. FOV of each camera node is a sector sensing region.When the tracked target leaves the FOV, the correspondingcamera node terminates the detection and extraction of thetarget. Besides, target occlusions could be resolved by thecollaboration of two or more camera nodes, which is notaddressed in this work. Figure 1 depicts the results of targetdetection and trajectory extraction using the video sequencesfrom two camera nodes with different views. The test resultsindicate that the method seems to be effective and practicalin camera nodes. In Figure 1(a), there are two people walkingalong the road. Figure 1(b) shows that both of them can beproperly detected by the proposed background subtraction.As can be seen in Figure 1(c), the trajectories are correctlyextracted by combining the target blob tracking and huehistogram matching.
2.2. Trajectory Representation. Symbolic aggregate approxi-mation (SAX), an efficient symbolic representation, convertstime series data to a string consisting of a finite alphabetΣ according to a set of well-defined rules [14]. Given the
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(a) (b) (c)
Figure 1: Target detection and trajectory extraction from two different views. (a) The original frame. (b) The results of the target detection.(c) The results of trajectory extraction.
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Figure 2: Example of trajectory representation. (a) The sequenceof Euclidean distance between two adjacent positions. (b) Thecorresponding symbolic representation.
in horizontal and vertical axes.Thus the difference sequencesbetween the coordinates of two adjacent time instants aredescribed as Δx = {Δ𝑥
1, Δ𝑥2, . . . , Δ𝑥
𝑡−1} and Δy = {Δ𝑦
1,
Δ𝑦2, . . . , Δ𝑦
𝑡−1}, where Δ𝑥
𝑖−1= 𝑥𝑖−𝑥𝑖−1
andΔ𝑦𝑖−1
= 𝑦𝑖−𝑦𝑖−1
(𝑖 = 2, 3, . . . , 𝑡). The sequence of Euclidean distance betweenthe coordinates of two adjacent time instants is taken intoaccount to analyze targetmotion, and SAX continuously con-verts a sliding window of the sequence to symbols. Supposethat a subset of the sequence fallingwithin the slidingwindow
is r = {Δ𝑟1, Δ𝑟2, . . . , Δ𝑟
𝑛}, where Δ𝑟
𝑖= √Δ𝑥
2
𝑖+ Δ𝑦2
𝑖and 𝑛
denotes the sliding window size.The symbolic representationis exemplified by the following procedures.The input is r andthe output is a symbol sequence s = {𝑠
1, 𝑠2, . . . , 𝑠
𝑚}(0 < 𝑚 ≤
𝑛), 𝑠𝑖∈ Σ, and 𝑚 is SAX symbol size. Firstly, r is normalized
to a sequence with a mean of zero and a standard deviationof one. Secondly, the sequence is divided into 𝑚 equal-sizedsections and the mean value of each section is calculated.Thepiecewise aggregate approximation (PAA) representationcomprises the vector of these mean values. Finally, SAX usesa sorted list of breakpoints 𝛽
𝑗(𝑗 = 1, . . . , 𝑁), where 𝑁
is alphabet size, to transform the PAA into a sequence ofequiprobable symbols according to the range of the PAAcoefficients. More details of the symbolic transformation areavailable in the SAX literature [15].
An example of trajectory representation with the abovemethod is illustrated in Figure 2. According to the trajectoryof a person, we can compute the subset of Euclidean dis-tance between adjacent position coordinates, r, as shown inFigure 2(a). Figure 2(b) shows the corresponding symbolicrepresentation. r is discretized by obtaining a PAA approxi-mation and then the predetermined breakpoints are used totransform the PAA coefficients into symbols. In the example,with 𝑛 = 50, 𝑚 = 10, and alphabet size 𝑁 = 5, r is mappedto the symbol sequence “dbdadcccbc.” As can be seen, SAXcreates an approximation of the trajectory by reducing itsoriginal size while keeping the essential features.
3. Abnormal Behavior Detection
3.1. Anomaly Transition Model. The trajectory of movingtarget provides important informationwhich can be analyzedto detect anomaly behavior. Generally, normal behavioris defined as calm movements, that is, a person moving
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Table 1: An example of an anomaly transition matrix with𝑁 = 5.
i 𝑖 + 1
a b c d ea 1.0000 0.7500 0.9000 0.3500 1.0000b 1.0000 0.8676 0.5846 0.6324 0.9154c 0.9978 0.6525 0.5740 0.7825 0.9933d 0.9322 0.7542 0.4195 0.8941 1.0000e 0.9600 0.4400 0.6000 1.0000 1.0000
relatively slowly across the scene. In this case, target trajectorycan be presented as a regular sequence of position coordi-nates; Δ𝑥
𝑖and Δ𝑦
𝑖will not change drastically. Conversely, an
abnormal event is likely to be accompanied by more rapidor irregular movements, such as running and abrupt changeof velocity. In this paper, a first-order Markov model is usedto model the symbol transition based on a collection ofpreviously observed data corresponding to normal behavior.
As described in Section 2.2, target trajectory extractedfrom blobs tracking must be converted to a symbol sequence.Let s = {𝑠
1, 𝑠2, . . . , 𝑠
𝑚} be a SAX symbol sequence and
Σ = {𝛼1, 𝛼2, . . . , 𝛼
𝑁} an alphabet set; the symbol transition
probability is defined as
𝜋 (𝛼𝑖, 𝛼𝑗) = 𝑃 (𝑠
𝑖+1= 𝛼𝑗| 𝑠𝑖= 𝛼𝑖) . (1)
Accordingly, the anomaly transition probability of symbolsequence is described by
𝜋𝐴(𝛼𝑖, 𝛼𝑗) = 1 − 𝜋 (𝛼
𝑖, 𝛼𝑗) =
𝑁
∑
𝑘=1
𝜋 (𝛼𝑖, 𝛼𝑘) , 𝑘 = 𝑗. (2)
Thus the anomaly transition matrix on the symbol set Σ canbe denoted as
Π𝐴= (𝜋𝐴(𝛼𝑖, 𝛼𝑗)) . (3)
Table 1 illustrates the anomaly transition matrix of thetrajectory of a walking person; it can be seen that the abnor-mal transition between symbols has different probability. Forexample, the anomaly transition probability of two symbolpatterns “da” and “dc” is 0.9322 and 0.4195, respectively. Thismeans that the abnormal degree of the former is greater thanthe latter’s.
3.2. Anomaly Detection. From the above description,anomaly probability of a SAX symbol sequence can bemeasured by the anomaly transition probability of thesequence. Given a SAX symbol sequence s = {𝑠
1, 𝑠2, . . . , 𝑠
𝑚},
the anomaly probability is computed as
𝑃𝐴 (
s) = 𝑃 (𝑠1) 𝜋𝐴(𝑠1, 𝑠2) 𝜋𝐴(𝑠2, 𝑠3) ⋅ ⋅ ⋅ 𝜋
𝐴(𝑠𝑚−1
, 𝑠𝑚) , (4)
where 𝑃(𝑠1) is the prior probability and 𝜋
𝐴is the anomaly
transition probability given by (2).When an abnormal behav-ior is detected, the target trajectory deviates from the normalrange and reflects a higher abnormal degree. The anomalyprobability of the corresponding symbol sequence therefore
is relatively large. Obviously, the greater the anomaly proba-bility is, the more likely an abnormal behavior occurs.
According to (4), an anomaly detection threshold shouldbe predefined to judge whether an abnormal behavior occurs.The threshold is defined as, under normal conditions, themaximum anomaly probability of SAX symbol sequencewith the same length. For each camera node, its detectionthreshold is determined by learning the training set whichcorresponds to symbol sequences gained from target tra-jectories without abnormal behavior. Assume that s
𝑡is the
training sequence set and its 𝐿 subsets are denoted as {s𝑡,𝑙}(𝑙 =
1, 2, . . . , 𝐿). Hence the detection threshold can be approachedby 𝐿 times of iterations:
𝛿 = max (𝛿, 𝑃𝐴(s𝑡,𝑙)) . (5)
Thereafter, any symbol sequence whose anomaly proba-bility exceeds the threshold is treated as a possible abnormalbehavior. For a trajectory to be analyzed, if the symbolsequence is expressed as s
𝑑, the local decision can be deter-
mined by
𝐷 = {
0, if 𝑃𝐴(s𝑑) ≤ 𝛿,
1, otherwise,(6)
where 𝐷 = 1 indicates that an abnormal behavior occurswhile𝐷 = 0 indicates that no anomaly is detected.
3.3. Node Communication and Voting. As a rule, a localdecision from single camera node is likely to be unreliableunder some specific circumstances. Because of the influencesof occlusions, illumination change, and cluttered backgrounddistraction, a certain camera nodemay provide the trajectoryinformation that deviates from actual values, which leadsto wrong decision making. Therefore, it is necessary forabnormal behavior detection in camera sensor networks tohave fault-tolerant ability to exclude the risk of false alarms.Referring to [16], the majority voting is adopted to make thefinal decision. In order to implement the voting, the cameranode needs to collect all local decisions about the behavior ofthe same target.
If the FOVs of camera nodes are mutually overlapped,we define that these nodes are correlated. In this paper, it isassumed that the relative positions among the camera nodesare determined initially and each camera node knows itscorrelated nodes. If a camera node firstly detects a movingtarget, it will extract the target blob and produce the huehistogram of the blob. Then the camera node broadcasts thehue histogram to all the correlated nodes. At the same time,target blobs within the FOVs of the correlated nodes areextracted and the hue histograms of the segmented blobs areproduced. When the correlated nodes receive the broadcastmessage, they will search for the corresponding target fromthe detected blobs. Due to the fact that each target’s huehistogram retains its form between multiple camera nodesregardless of perspective effects, the correspondence betweentargets in different FOVs can be established by histogrammatching. For a certain camera node, its correlated nodesthat can detect the same target are seen as effective neighbors
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Figure 3:Anomaly detection fromcamera 1with 𝛿 = 0.0227. (a)Thesequence of Euclidean distance. (b) Anomaly detection probability.The solid line represents the detection threshold.
of the camera node. After the effective neighbors returnone-byte acknowledgment message, the camera node canknow its effective neighbors list and count the number ofeffective neighbors. Once the camera node reaches a decisionabout target trajectory, it will listen to the judge results fromits effective neighbors. Note that a single bit, either 1 or0, is used to represent a local decision. The camera nodewill report the abnormal behavior and raise an alert only ifpositive voting rate exceeds half of the total number of itseffective neighbors. Additionally, the hue histograms used inthis paper are of 18 bins and the number of pixels in eachbin requires two bytes for representation. The informationexchange between camera nodes is implemented by awirelesstransceiver mounted on camera sensor board. For anomalydetection of a specific target, the foregoing indicates that thecommunication overhead of the entire camera networks is36 + 𝑘 bytes and extra 𝑘 bits, where 𝑘 denotes the numberof effective neighbors. Obviously, our method has a lowcommunication cost.
4. Experimental Results
The proposed method is tested with actual sequences cap-tured from six cameras in an outdoor environment. Theresolution of the cameras is set at 320 × 240 pixels and theframe rate is 25 fps. The anomaly detection using trajectoryanalysis is divided into three phases. First, the sequence ofEuclidean distance of target trajectory extracted from theraw image is trained and modeled in order to obtain theanomaly detection threshold. Second, abnormal behaviordetection is performed by comparing anomaly probability oftesting trajectories and the detection threshold. Third, thelocal decisions of the individual camera nodes are used tomake a majority decision.
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Figure 4: Anomaly detection from camera 2 with 𝛿 = 0.0235.
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Figure 5: Anomaly detection from Camera 3 with 𝛿 = 0.0199.
4.1. Effectiveness Evaluation. To demonstrate the effective-ness of the proposedmethod, we define two experiments withdifferent types of abnormal behaviors. In the experiments,the number of trajectories for the training is 18 and eachtrajectory length is 220. Besides, the sliding window size 𝑛 =
50, SAX symbol size𝑚 = 10, alphabet size𝑁 = 5, and the datastep of training is 5. Under these circumstances, the numberof training is 630.
The first experiment considers a sequence of 500 frameswhere a person is walking along a road except two timeintervals. The person suddenly starts running twice fromframes 101 and 476.We regardwalk as normal behavior while
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Figure 6: The decision on abnormal behavior “run”. (a)–(c) show the local decision from three camera nodes, respectively. (d) shows thevoting result.
run as abnormal behavior. Due to space constraints, onlythree out of six camera nodes are explained. The sequencesof Euclidean distance of the extracted trajectories from threedifferent views are illustrated in Figures 3(a)–5(a), respec-tively. As can be seen, the Euclidean distance has larger valuesand remains relatively stable when the target is running. Forthree camera nodes, the results of anomaly detection areshown separately in Figures 3(b)–5(b). From Figure 3(b), thedetection threshold of camera 1 is 0.0227, which is determinedby training the sequences corresponding to normal behaviorand denoted by the solid line. It can be seen that the anomalydetection probability is above the detection threshold duringframes 116 to 150 and frames 491 to the end. This means thatthe camera 1 is able to identify correctly the anomaly twice.By contrast, the anomaly detection of camera 2 is unreliable,as shown in Figure 4(b). The detection threshold of camera 2is 0.0235, under the condition that camera 2 only detects thefirst abnormal behavior from frames 126 to 150whereas it failsto find the second. Similarly, it can be noticed that the resultof camera 3 deviates from the truth in a few cases, as shown inFigure 5(b). Camera 3 detects the second abnormal behaviorat frame 491. Conversely, the first is wrongly treated as two
time intervals, frames 111 to 135 and frames 141 to 150. In otherwords, the abnormal behavior corresponding to frames 136 to140 is labeled as normal behavior.
From above descriptions, the anomaly detection from thedifferent views may vary and the result of individual cameranode is even absolutely wrong. To demonstrate the feasibilityand effectiveness of the voting mechanism, the detectionresult of each camera node is further analyzed. According to(6), the local decisions of three camera nodes are depictedin Figures 6(a)–6(c). Besides, the final decision can be madethrough the majority voting. That is, if two or more localdecisions are true, the final decision will be true, and viceversa. The final decision is shown in Figure 6(d), wherethe two time intervals of abnormal behavior correspondto frames 116 to 150 and frames 491 to 500, respectively.Obviously, the voting result is much more reliable and closerto the reality.
The second experiment is tested on another scenario of236 frames, which concerns a woman walking ahead and aman behind her.Theman suddenly accelerates and overtakesher at frame 191. In this test, overtake is regarded as abnormalbehavior. As before, we analyze the trajectory of the man and
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Figure 7: Anomaly detection from three camera nodes. (a) Camera1 with 𝛿 = 0.0105. (b) Camera 2 with 𝛿 = 0.0144. (c) Camera 3 with𝛿 = 0.0196.
Figure 7 illustrates the anomaly detection of three cameranodes. As can be seen, the detection thresholds of threecamera nodes are 0.0105, 0.0144, and 0.0196, respectively. Allof three camera nodes capture the abnormal condition atdifferentmoments, whereas camera 2makes awrong decisionon the subsequent test sequence. Comparing with the localdecisions, the final decision seems to be convincing, as shownin Figure 8. It should be noted that different camera nodesindicate anomaly at different times and during different timeperiods. The main reason is that the cameras observe thetarget from different views. As a result, the target detectionand trajectory analysis are affected in varying degrees.
4.2. Performance Analysis. To demonstrate quantitatively theperformance of abnormal behavior detection, we comparedour method with two classical approaches described asfollows. (1) Similarity-based approach (SA): this approachdescribes target trajectory in terms of a sequence of featurevectors where a feature vector represents both the positionand velocity of the target. Anomaly detection is by examiningthe distance between the input and predefined patterns whichis the nearest to the input vector by the Euclidean metric.If the distance exceeds a threshold, the input vector is con-sidered abnormal; otherwise, the input vector is considerednormal. (2) Model-based approach (MA): the basic idea isthat the useful features of observations, such as the opticalflow and the target size, are extracted and fed into HMM
in order to model normal behavior. Anomaly detection canbe done by estimating an observation sequence with thetrained HMM. If the HMM yields a low likelihood valuefor the observation sequence, it is likely that the sequencerepresents abnormal behavior. We evaluate the reliability ofabnormal behavior detection using the ROC curve, whichplots the detection rate against the false alarm rate. Theformer is defined as the ratio of the number of abnormalbehaviors which are correctly detected to the total number ofabnormal behaviors, and the latter is the ratio of the numberof normal behaviors that are incorrectly detected as abnormalbehaviors to the total number of normal behaviors.The num-ber of testing trajectories is 54, each sequence is composedof normal and abnormal behaviors. Anomaly detection isimplemented in single camera node and the comparisonis based on the averaged results. Figure 9 represents ROCcurves illustrating the detection rate and the false alarmrate of three methods. It can be observed that the detectionreliability of the proposed method is very similar to thatof the MA while obviously it is higher than that of theSA. Additionally, we analyze the runtime of three methods,which can intuitively reflect the computation complexity.Theexperiment results show that the average runtime of SA,MA, and the proposed method is 65.8, 147.1, and 99.9ms,respectively. By contrast, although the MA is a little over theproposed method in the detection rate, it is time consumingand requires more computational resources. Meanwhile, thecomputation efficiency of ourmethod is lower than that of theSA, but our method has a distinct advantage over the SA indetecting abnormal behavior. Therefore, it can be concludedthat the proposed method outperforms two other methodsbecause of having the best tradeoff between the detectionreliability and computation complexity.
The following experiments are conducted to investigatethe effect of varying the number of camera nodes on theperformance of the proposed method. Figure 10 shows theROC curves of our method under different number ofcamera nodes. We can see from this figure that the proposedmethod has a high detection rate and a low false alarm rate,which validates the effectiveness of symbolic representationof target trajectory and anomaly probability detection basedon Markov model. For example, when the number of cameranodes is 3, the proposedmethod can achieve a high detectionrate of 93.7% and a low false alarm rate of 7.2%. From theresults shown in Figure 10, when the number of camera nodesincreases, the performance of the proposedmethod increasesaccordingly. The main reason is that the fault-tolerant abilityof anomaly detection is strengthened by utilizing the vot-ing among multiple camera nodes. Furthermore, it can beseen that the algorithm performance has no tendency toimprove when the number of camera nodes increases to five.In addition, we notice that computational costs for nodecollaboration increase moderately as camera nodes increase.This is because the time required for target matching indifferent FOVs using hue histogram is dependent on thenumber of neighboring nodes. Note that the average runtimeof the proposedmethod for six camera nodes is 184.2ms.Thecomparison and analysis show that the proposed method isfeasible for camera sensor networks.
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Figure 8: The decision on abnormal behavior “overtake.” (a)–(c) show the local decision from three camera nodes, respectively. (d) showsthe voting result.
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International Journal of Distributed Sensor Networks 9
5. Conclusions
This paper addresses an approach to abnormal behaviordetection based on trajectory analysis.TheEuclidean distancebetween two adjacent coordinates is represented as a symbolsequence. A first-order Markov model is used to build theanomaly transitionmatrix and the detection threshold can bedetermined by training the obtained symbol sequences. Thevotingmechanism reaches a final decision in accordancewithlocal decisions of camera nodes. The experimental resultsshow that the SAX representation of target trajectory iseffective and the proposed method possesses relatively highreliability of abnormal behavior detection. Moreover, ourapproach is proper for camera sensor networks due to thelightweight communication overhead and less computationcomplexity.
However, the anomaly detection proposed by this paper isdirectly related to target detection and trajectory extraction.If target trajectory largely deviates from the actual condition,the anomaly detection will be wrong. It is undesirablefor complex abnormal behavior detection to only considerthe trajectory information. Therefore, it is vital to find arepresentative and sufficient description of the training datacorresponding to normal behavior. Although the proposedmethod is able to detect abnormal behavior in differentscenarios, we find that the SAX parameters in training anddetection phase have influences on the performance and theyare hard to estimate. Our future work will focus on this issue.
Conflict of Interests
The authors declare that there is no conflict of interestsregarding the publication of this paper.
Acknowledgments
This work was supported by the Natural Science Foundationof China under Grants 41202232 and 61271274 and theResearch Program of Hubei province, China, under Grants2012FFA108 and 2013BHE009.
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