The method of people counting has been developed using two different approaches. In the direct approach (also called detection-based), people in the scene are first individually detected, using some form of segmentation and object detection, and then counted. In the indirect approach (also called map-based), instead, counting is performed using the measurement of some feature that does not require the separate detection of each person in the scene. This paper focuses on human detection first and comprehensive review on the two methods of people counting

A, Kowcika.

International Journal of Innovative Technology and Research (IJITR)

Proceedings of the International Conference , “Computational Systems for Health & Sustainability”17-18, April, 2015 - by R.V.College of Engineering,Bangalore,Karnataka,PIN-560059,INDIAAll Copyrights Reserved by R.V. College of Engineering, Bangalore, Karnataka Page | 34People Counting System- A ReviewKowcika.AAsst.Professor, DEPARTMENT OF CSER.V.College of Engineering, BANGALORE 560059 IndiaAbstract: The method of people counting has been developed using two different approaches. In the direct approach (also calleddetection-based), people in the scene are first individually detected, using some form of segmentation and object detection, and thencounted. In the indirect approach (also called map-based), instead, counting is performed using the measurement of some feature thatdoes not require the separate detection of each person in the scene. This paper focuses on human detection first and comprehensivereview on the two methods of people counting.INTRODUCTIONPeople count Extraction is a crucial and challenging problemin visual surveillance. For instance an accurate and real-timeestimation of number of people present in a shopping mallprovides significant customer information for managers.Further, automatic monitoring of the people counts in publicareas is essential for safety control and urban planning. Inrecent years, though this problem domain is well explored; yetthe following issues are to be resolved such as videos thatconsist of stationary objects, complicated background, andlow resolution. However, real scenes always include bothmoving/stationary human beings, the complicated background,and most videos in a visual surveillance system have arelatively low resolution. The method of People countextraction falls under two categories based on the Purpose [1].(a).Estimate the number of people in a region of interest (ROIcounting). (b).To count the crowds across a line of interest(LOI counting).The two different methods for extracting the number People incrowds are Detection-based methods and Map-based methods.Detection based methods determine the number of people andtheir locations using some form of segmentation and humandetection such as head, shoulder, head-tops .Map-basedmethods exploits the relationship between the people andlocal/global features from the image. [2].Some of the applications to show the importance of peoplecount are as follows,• Occupancy limit in a building.• To actively manage city services.• Allocate resources for public events.• To aid with crowd control during rallies.• Crowd disaster prevention.• Group behavior modeling.• To provide usage statistics to metro operators.• An accurate and real-time estimation of people in ashopping mall can provide valuable information formanagers.• Tourists flow estimation.Challenges of people counting:Crowd scenes remain particularly challenging for the detectionand tracking tasks due to the following reasons,a) Heavy occlusions, high person densities andsignificant variation in people’s appearance.b) The ambiguous appearance of body parts, e.g.the head of one person could be similar to ashoulder of a near-by person.c) People must be moving, the background must besimple, and the image resolution must be high.d) Varying background and lighting condition.e) Inter-object occlusion, Scene occlusion ,Cluttered backgroundf) People can have a wide range of scalesProceedings of the International Conference , “Computational Systems for Health & Sustainability”17-18, April, 2015 - by R.V.College of Engineering,Bangalore,Karnataka,PIN-560059,INDIAAll Copyrights Reserved by R.V. College of Engineering, Bangalore, Karnataka Page | 35Figure.1Human Detection System:Human Detection can be performed as two step process. Thefirst step is to detect the moving object followed byclassification. Object detection could be performed usingbackground subtraction, optical flow and spatio-temporalfiltering techniques. A moving object could be classified as ahuman being using shape-based, texture-based or motion-based features. A comprehensive review of availabletechniques for detecting human beings in surveillance videosis presented in this paper [3].The people count can be achieved by implementing followingmodules, Background Subtraction Physical/High Feature Extraction or detection ofbody parts (Detection based or Map based method) Applying Supervised/Unsupervised LearningAlgorithmsDetection Based Method for People count:In the existing approaches extraction of people count isimplemented using the algorithms such as EM. For exampleHou et al used the Expectation Maximization (EM)-basedalgorithms to locate individuals in a low resolution scene.Though this approach is   accurate in both counting anddetection it does not distinguish human and non-humanobjects more accurately in a low resolution videos [2].Rodriguez et al explored constraints imposed by the crowddensity and formulate person detection as the optimization of ajoint energy function combining crowd density estimation andthe localization of individual people. They demonstrated howthe optimization of such an energy function significantlyimproves person detection and tracking in crowds. Currentlyvideo frames are processed individually and obtaineddetections are tracked in post-processing that is duringtracking but not in detecting [4].Xu et al presents a counting system which consists of fourmodules: foreground extraction, head-shoulder componentdetection, tracking and trajectory analysis. It reduced thecomputation costs and cope with various complex surveillancesituations for foreground extraction, an adaptive componentsnumber selection strategy for mixture of Gaussians model isproposed. Pedestrians are detected by their head shoulders,because this part is less varied and less likely occluded from adownward-slope view.  Each pedestrian is tracked throughconsecutive frames using the Kalman filter techniques andcost function.  The resulting trajectories are analyzed to countpeople entering or leaving the scene But the limitations of thisapproach lies in handling Occlusions [7].Merad et al detection based method where the skeletonsilhouette is computed and decomposed into a set of segmentscorresponding to the head, torso and limbs. This structurecaptures the minimal information about the skeleton shape. Noassumption is made about the view point; this is done after thehead pose process. Several results present the efficiency of thelabeling process, particularly its structural properties for thedetection of heads within a crowd. The experimental resultsproved that the challenges (e) and (f) are not faced by thisapproach [8].Bhondi et al proposed a system for automatic people countingin crowded environments. The proposed approach is acounting-by-detection method based on depth imagery. It isdesigned to be deployed as an autonomous appliance forcrowd analysis in video surveillance application scenarios.This system performs foreground/background segmentation ondepth image streams in order to coarsely segment persons, andthen depth information is used to localize head candidateswhich are then tracked in time on an automatically estimatedground plane. An extensive comparative evaluation isdiscussed. Though this method handled crowds, queuing andgroups well the challenges (b), (c) and (d) are not highlighted[9].Map Based Method for People count:Zhang et al used a complex network-based algorithm to detectinterest points and extract the global texture features inscenarios. After this point degree matrices is computed andstatistical measures are obtained. Then it establishes amapping between the moving interest point’s features and thenumber of people in a crowd scene. This method is mucheffective only for specific test cases and also towardsOcclusion [5].Hou et al proposed a method in which is more accurate in bothcounting and detection. First, post-processing steps areperformed on background subtraction results to estimate thenumber of people in a complicated scene, which includespeople who are moving only slightly. Second, an ExpectationProceedings of the International Conference , “Computational Systems for Health & Sustainability”17-18, April, 2015 - by R.V.College of Engineering,Bangalore,Karnataka,PIN-560059,INDIAAll Copyrights Reserved by R.V. College of Engineering, Bangalore, Karnataka Page | 36Maximization (EM)-based method has been developed tolocate individuals in a low resolution scene. In this process, anew cluster model is used to represent each person in thescene. This process does not require a very accurateforeground contour. Third, the number of people is used as apriori for locating individuals based on feature points. Hence,the methods for estimating the number of people and forlocating individuals are connected. However, this approachdistinguished human and non-human objects more accurately;a high-resolution video is needed to provide sufficient data[6].Hussain et al [10] employ pixel-based approaches for featureextraction and back- propagation neural network for peoplecounting and estimation for crowd density at Masjidal-Haram(Mosque). The experimental results indicate high accuracyand performance level for very low to moderate crowd.However, when the density of the crowd increases, thedetection rate tends to decrease due to occlusion levelchanges.CONCLUSIONThe purpose of this review article is to present  two differentapproaches,  the direct approach (also called detection-based),people in the scene are first individually detected, using someform of segmentation and object detection, and then counted.In the indirect approach (also called map-based), instead,counting is performed using the measurement of some featurethat does not require the separate detection of each person inthe scene. This paper focuses on human detection first andcomprehensive review on the two methods of people counting.The idea of presenting this review is the first step to do moreresearch in this direction to come out with some innovativeresearch findings soon.ACKNOWLEDGMENTThe author is grateful to Research Guide, Dr.S.Sridhar,Professor and Dean-Cognitive and Central Computing, of ourcollege for the guidance to prepare this review article and alsothankful to Dr.G.Shobha, HoD/CSE and Dr.N.K.Srinath, PGDean (CSE) for the support offered.REFERENCES[1]. Li, Jingwen, L. Huang, C.Liu,"Robust people countingin video surveillance: Dataset and system." in 8th IEEEInt. Conf. on Advanced Video and Signal-BasedSurveillance, pp. 54-59, 2011.[2]. Hou, Ya-Li, Grantham KH Pang. "People counting andhuman detection in a challenging situation", in Part A:Systems and Humans, IEEE Trans. on Systems, Manand Cybernetics, 41, no. 1, 24-33, 2011.[3]. Paul, M., Haque, S.M.E., Chakraborty, S,”Humandetection in surveillance video and its applications — areview” EURASIP Journal on Advanced in SignalProcessing 2013, pp.1–16 ,2013.[4]. Rodriguez, Mikel, Ivan Laptev, Josef Sivic, and J-Y.Audibert. "Density-aware person detection and trackingin crowds." In Computer Vision (ICCV), 2011 IEEEInternational Conference on, pp. 2423-2430. IEEE,2011.[5]. Zhang, Yueguo, L.Dong, Jianhua Li, Shenghong Li,and Z. J. Gao. "A complex network-based approach toestimating the number of people in video surveillance",in IEEE Int. Symp.  on Broadband Multimedia Systemsand Broadcasting, pp. 1-4, 2013.[6]. Hou, Ya-Li, Grantham KH Pang. "People counting andhuman detection in a challenging situation", in Part A:Systems and Humans, IEEE Trans. on Systems, Manand Cybernetics, 41, no. 1, 24-33, 2011.[7]. Xu, Huazhong, Pei Lv, and Lei Meng. "A peoplecounting system based on head-shoulder detection andtracking in surveillance video." Computer Design andApplications (ICCDA), 2010 International Conferenceon. Vol. 1. IEEE, 2010.[8]. Merad, Djamel, K-E. Aziz, and Nicolas Thome. "Fastpeople counting using head detection from skeletongraph." Advanced Video and Signal Based Surveillance(AVSS), 2010 Seventh IEEE International Conferenceon. IEEE, 2010.[9]. Bondi, Enrico, et al. "Real-time people counting fromdepth imagery of crowded environments." AdvancedVideo and Signal Based Surveillance (AVSS), 201411th IEEE International Conference on. IEEE, 2014.[10]. Hussain et al,"CDES: A pixel-based crowd densityestimation system for Masjid al-Haram." in Safetyscience 49, no. 6 (2011): 824-833.

PEOPLE COUNTING SYSTEM- A REVIEW

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PEOPLE COUNTING SYSTEM- A REVIEW

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