Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5686096
J. Ayoub, B. Granado, O. Romain, Y. Mohanna
In this paper we focus on the recognition of threedimensional objects captured by an active stereo vision sensor. The study is related to our research project Cyclope, this embedded sensor based on active stereo-vision approach allows real time 3D objects reconstruction. Our medical application requires differentiation between hyperplastic and adenomatous polyps during 3D endoscopic imaging. The detection algorithm consists of SVM classifier trained on robust feature descriptors of a surfacic 3D point cloud extracted from the surface of studied object. We compared our feature extraction method with others. Experimental results were encouraging and show correct classification rate of approximately 97%. The work contains many techniques concerning image processing and system calibration and provides detailed statistics about the detection rate and the computing complexity.
{"title":"3-D object recognition based on SVM and stereo-vision: Application in endoscopic imaging","authors":"J. Ayoub, B. Granado, O. Romain, Y. Mohanna","doi":"10.1109/SOCPAR.2010.5686096","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686096","url":null,"abstract":"In this paper we focus on the recognition of threedimensional objects captured by an active stereo vision sensor. The study is related to our research project Cyclope, this embedded sensor based on active stereo-vision approach allows real time 3D objects reconstruction. Our medical application requires differentiation between hyperplastic and adenomatous polyps during 3D endoscopic imaging. The detection algorithm consists of SVM classifier trained on robust feature descriptors of a surfacic 3D point cloud extracted from the surface of studied object. We compared our feature extraction method with others. Experimental results were encouraging and show correct classification rate of approximately 97%. The work contains many techniques concerning image processing and system calibration and provides detailed statistics about the detection rate and the computing complexity.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129742070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5686088
C. Lopez, V. Prince, M. Roche
Automatic titling (i.e. providing titles) is one of key domains of Web site accessibility. This paper provides an approach allowing the automatic titling of texts (e.g. emails, fora, etc.) relying on the morphosyntactic study of human written titles in a corpus of various texts. The method is developed in four stages: Corpus acquisition, candidate sentences determination for titling, noun phrase extraction in the candidate sentences, and finally, selecting a particular noun phrase to play the role of the text title (ChTitres approach). The method has been evaluated by ten users, and the satisfaction enquiry shows that the titles selected through this process are relevant.
{"title":"Automatic titling of electronic documents with noun phrase extraction","authors":"C. Lopez, V. Prince, M. Roche","doi":"10.1109/SOCPAR.2010.5686088","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686088","url":null,"abstract":"Automatic titling (i.e. providing titles) is one of key domains of Web site accessibility. This paper provides an approach allowing the automatic titling of texts (e.g. emails, fora, etc.) relying on the morphosyntactic study of human written titles in a corpus of various texts. The method is developed in four stages: Corpus acquisition, candidate sentences determination for titling, noun phrase extraction in the candidate sentences, and finally, selecting a particular noun phrase to play the role of the text title (ChTitres approach). The method has been evaluated by ten users, and the satisfaction enquiry shows that the titles selected through this process are relevant.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129275927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5686078
A. Sagheer
In this paper we focus on dealing with large size databases. Such databases require the construction of suitable feature spaces to accommodate data. The paper presents a new search algorithm based on the self organizing map (SOM) avoids the high-cost of computation in such cases. The proposed SOM algorithm is combined with support vector machine (SVM) to form a new appearance based approach. The proposed approach is evaluated in face recognition experiments across variations in pose and illumination. A huge-size database is used to judge effectively the proposed approach. The results have compared with another reported approach based on light field theory using same huge database.
{"title":"Improved SOM search algorithm for high-dimensional data with application to face recognition across pose and illumination","authors":"A. Sagheer","doi":"10.1109/SOCPAR.2010.5686078","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686078","url":null,"abstract":"In this paper we focus on dealing with large size databases. Such databases require the construction of suitable feature spaces to accommodate data. The paper presents a new search algorithm based on the self organizing map (SOM) avoids the high-cost of computation in such cases. The proposed SOM algorithm is combined with support vector machine (SVM) to form a new appearance based approach. The proposed approach is evaluated in face recognition experiments across variations in pose and illumination. A huge-size database is used to judge effectively the proposed approach. The results have compared with another reported approach based on light field theory using same huge database.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114622443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5686627
A. Zehtabian, H. Hassanpour, Shahrokh Zehtabian, V. Zarzoso
The Singular Value Decomposition (SVD) is a powerful tool used for subspace division. In this paper a novel approach for speech signal enhancement is presented which is based on SVD and Genetic Algorithm (GA). The method is derived from the effects of environmental noises on the singular vectors as well as the singular values of a clean speech. This article reviews the existing approaches for subspace estimation and proposes novel techniques for effectively enhancing the singular values and vectors of a noisy speech. The proposed approach clearly results in a considerable attenuation of the noise as well as retrieving the quality of the original speech. The efficiency of our proposed method is affected by a number of crucial parameters which are optimally set by utilizing the GA. Extensive sets of experiments have been carried out for both of additive white Gaussian noise as well as different types of realistic colored noise cases. The results of applying six superior speech enhancement methods are then evaluated by the objective (SNR) and subjective (PESQ) measures.
{"title":"A novel speech enhancement approach based on Singular Value Decomposition and Genetic Algorithm","authors":"A. Zehtabian, H. Hassanpour, Shahrokh Zehtabian, V. Zarzoso","doi":"10.1109/SOCPAR.2010.5686627","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686627","url":null,"abstract":"The Singular Value Decomposition (SVD) is a powerful tool used for subspace division. In this paper a novel approach for speech signal enhancement is presented which is based on SVD and Genetic Algorithm (GA). The method is derived from the effects of environmental noises on the singular vectors as well as the singular values of a clean speech. This article reviews the existing approaches for subspace estimation and proposes novel techniques for effectively enhancing the singular values and vectors of a noisy speech. The proposed approach clearly results in a considerable attenuation of the noise as well as retrieving the quality of the original speech. The efficiency of our proposed method is affected by a number of crucial parameters which are optimally set by utilizing the GA. Extensive sets of experiments have been carried out for both of additive white Gaussian noise as well as different types of realistic colored noise cases. The results of applying six superior speech enhancement methods are then evaluated by the objective (SNR) and subjective (PESQ) measures.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124693510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5686412
Nadia Feddaoui, K. Hamrouni
Iris recognition has been recently given greater attention in human identification and it's becoming increasingly an active topic in research. This paper presents a personal identification method based on iris. The Method includes three steps. In the first one, the eye image is processed in order to obtain a segmented and normalized eye image by applying an integrodifferential operator, Hough transform and polar transformation. In the second step, the texture of iris image is analyzed by Gabor filters. Then, we have proposed a novel encoding method based on extracting invariant from local region of the iris to create an iris code of 144 bytes. We have studied different statistical descriptors of filtered image. We have calculated the modified Hamming distance between templates to find out the similarity between irises. The method is tested on the Casia v3 database. The experimental results illustrate the effectiveness of this coding in two modes of biometric iris: 100% of rank-one recognition rate and 1.97% of equal error rate in verification. Therefore the coding process is presented to achieve more satisfactory and convincing results than performed by traditional statistical based approaches and low storage requirements as an interesting alternative to Gabor phase coding.
{"title":"Iris recognition based on multi-block Gabor statistical features encoding","authors":"Nadia Feddaoui, K. Hamrouni","doi":"10.1109/SOCPAR.2010.5686412","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686412","url":null,"abstract":"Iris recognition has been recently given greater attention in human identification and it's becoming increasingly an active topic in research. This paper presents a personal identification method based on iris. The Method includes three steps. In the first one, the eye image is processed in order to obtain a segmented and normalized eye image by applying an integrodifferential operator, Hough transform and polar transformation. In the second step, the texture of iris image is analyzed by Gabor filters. Then, we have proposed a novel encoding method based on extracting invariant from local region of the iris to create an iris code of 144 bytes. We have studied different statistical descriptors of filtered image. We have calculated the modified Hamming distance between templates to find out the similarity between irises. The method is tested on the Casia v3 database. The experimental results illustrate the effectiveness of this coding in two modes of biometric iris: 100% of rank-one recognition rate and 1.97% of equal error rate in verification. Therefore the coding process is presented to achieve more satisfactory and convincing results than performed by traditional statistical based approaches and low storage requirements as an interesting alternative to Gabor phase coding.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5685843
Waheeda Al-Mayyan, H. Own, H. Zedan
This paper presents an iris recognition method based on the two dimensional dual-tree complex wavelet transform (2D-CWT) and the support vector machines (SVM). 2D-CWT has such significant properties as the approximate shift-invariance, high directional selectivity and computationally much more efficient. These properties are very useful in invariant iris recognition. SVM is used as a classifier and several kernel functions are tested in the experiments. The obtained experimental results showed that the proposed approach enhanced the classification accuracy. The experimental results were also compared with the k-NN and Naïve Bayes classifiers to demonstrate the efficacy of the proposed technique.
{"title":"Iris features extraction using dual-tree complex wavelet transform","authors":"Waheeda Al-Mayyan, H. Own, H. Zedan","doi":"10.1109/SOCPAR.2010.5685843","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5685843","url":null,"abstract":"This paper presents an iris recognition method based on the two dimensional dual-tree complex wavelet transform (2D-CWT) and the support vector machines (SVM). 2D-CWT has such significant properties as the approximate shift-invariance, high directional selectivity and computationally much more efficient. These properties are very useful in invariant iris recognition. SVM is used as a classifier and several kernel functions are tested in the experiments. The obtained experimental results showed that the proposed approach enhanced the classification accuracy. The experimental results were also compared with the k-NN and Naïve Bayes classifiers to demonstrate the efficacy of the proposed technique.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129942501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5686103
Nicolas Labroche, C. Marsala
In the recent years, forests of decision trees have seen an increasing interest from the Machine Learning community since they allow to aggregate the decisions from a set of decision trees into one robust answer. However, this approach suffers from two well-known limits: first, their performances depend on the number of trees and thus finding the right size and how to aggregate decisions could be very difficult and second, large forests loose the interpretability capacity of a single decision tree. In this paper, we propose a new approach in which decisions trees from a forest are clustered to simplify the overall decision process while maintaining a large amount of decision trees and to facilitate the interpretation of the results. The preliminary results that are presented in this paper show the effectiveness of our approach.
{"title":"Optimization of a fuzzy decision trees forest with artificial ant based clustering","authors":"Nicolas Labroche, C. Marsala","doi":"10.1109/SOCPAR.2010.5686103","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686103","url":null,"abstract":"In the recent years, forests of decision trees have seen an increasing interest from the Machine Learning community since they allow to aggregate the decisions from a set of decision trees into one robust answer. However, this approach suffers from two well-known limits: first, their performances depend on the number of trees and thus finding the right size and how to aggregate decisions could be very difficult and second, large forests loose the interpretability capacity of a single decision tree. In this paper, we propose a new approach in which decisions trees from a forest are clustered to simplify the overall decision process while maintaining a large amount of decision trees and to facilitate the interpretation of the results. The preliminary results that are presented in this paper show the effectiveness of our approach.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130458383","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5686495
R. Ebrahimpour, Naser Sadeghnejad, A. Amiri, Abolfazl Moshtagh
This paper presents an appropriate solution for low resolution faces recognition problem, using combination of diverse classifiers. We investigate our model based on extracting important features from low resolution images using three well known feature extractors; PCA, DCT and FFT, assigning MLP classifiers to each feature extractor and combining the votes of MLP classifiers using fusion of experts techniques. The results show that using the combination of three mentioned feature extractors and applying the Stack Generalization as combiner of classifiers for low resolution face recognition task, leads to higher performance than other recognition models. It's worth nothing that using a single feature extractor for low resolution face recognition task was failed in the previously surveys.
{"title":"Low resolution face recognition using combination of diverse classifiers","authors":"R. Ebrahimpour, Naser Sadeghnejad, A. Amiri, Abolfazl Moshtagh","doi":"10.1109/SOCPAR.2010.5686495","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686495","url":null,"abstract":"This paper presents an appropriate solution for low resolution faces recognition problem, using combination of diverse classifiers. We investigate our model based on extracting important features from low resolution images using three well known feature extractors; PCA, DCT and FFT, assigning MLP classifiers to each feature extractor and combining the votes of MLP classifiers using fusion of experts techniques. The results show that using the combination of three mentioned feature extractors and applying the Stack Generalization as combiner of classifiers for low resolution face recognition task, leads to higher performance than other recognition models. It's worth nothing that using a single feature extractor for low resolution face recognition task was failed in the previously surveys.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114455982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5686535
I. Aydin, Mehmet Karaköse, E. Akin
This paper presents a fast fuzzy algorithm, implemented using field-programmable gate array (FPGA) to detect the stator related faults in induction motors. Altera Cyclone III FPGA was employed for developing the proposed method. All coding of fuzzy algorithm was written using hardware description language (HDL) called very high speed integrated circuit description language (VHDL). Fuzzy system has three inputs and one output. The magnitudes of three phase current signals were given to the fuzzy system and stator related faults were detected. The validity of the proposed method was tested by experimental data set and was also compared to the results of MATLAB fuzzy logic toolbox. The experimental results show that the proposed method gives very efficient and reliable results for fault detection of induction motors.
本文提出了一种基于现场可编程门阵列(FPGA)的快速模糊算法来检测异步电动机定子相关故障。该方法采用Altera Cyclone III FPGA实现。模糊算法的所有编码都是用硬件描述语言(HDL)编写的，称为超高速集成电路描述语言(VHDL)。模糊系统有三个输入和一个输出。将三相电流信号的幅值输入模糊系统，检测定子相关故障。通过实验数据集验证了该方法的有效性，并与MATLAB模糊逻辑工具箱的结果进行了比较。实验结果表明，该方法对异步电动机的故障检测具有较高的效率和可靠性。
{"title":"FPGA based real time fuzzy fault detection algorithm","authors":"I. Aydin, Mehmet Karaköse, E. Akin","doi":"10.1109/SOCPAR.2010.5686535","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686535","url":null,"abstract":"This paper presents a fast fuzzy algorithm, implemented using field-programmable gate array (FPGA) to detect the stator related faults in induction motors. Altera Cyclone III FPGA was employed for developing the proposed method. All coding of fuzzy algorithm was written using hardware description language (HDL) called very high speed integrated circuit description language (VHDL). Fuzzy system has three inputs and one output. The magnitudes of three phase current signals were given to the fuzzy system and stator related faults were detected. The validity of the proposed method was tested by experimental data set and was also compared to the results of MATLAB fuzzy logic toolbox. The experimental results show that the proposed method gives very efficient and reliable results for fault detection of induction motors.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124504442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-12-01DOI: 10.1109/SOCPAR.2010.5686403
S. S. Pathan, A. Al-Hamadi, B. Michaelis
The governing behaviors of individuals in crowded places offer unique and difficult challenges, and limit the scope of conventional surveillance systems. In this paper, we investigate the crowd behaviors and localize the anomalies due to individual's abrupt dissipation. The novelty of the proposed approach can be described in three aspects. First, we introduce block-clips by sectioning the video segments into non-overlapping spatio-temporal patches to marginalize the arbitrarily complicated and dense flow field. Second, we treat the flow field in each block-clip as 2d distribution of samples and mixtures of Gaussian is used to parameterize it keeping generality of flow field intact. K-means algorithm is employed to initialize the mixture model and is followed by Expectation Maximization for optimization. These mixtures of Gaussian result in the distinct flow patterns precisely a sequence of dynamic patterns for each block-clip. Third, a bank of Conditional Random Field model is employed one for each block clip and is learned from the sequence of dynamic patterns and classifies each block-clip as normal and abnormal. We conduct experiment on two challenging benchmark crowd datasets PETS 2009 and University of Minnesota and results show that our method achieves higher recognition rates in detecting specific and overall crowd behaviors. In addition, the proposed approach shows dominating performance during the comparative analysis with similar approaches in crowd behavior detection.
{"title":"Crowd behavior detection by statistical modeling of motion patterns","authors":"S. S. Pathan, A. Al-Hamadi, B. Michaelis","doi":"10.1109/SOCPAR.2010.5686403","DOIUrl":"https://doi.org/10.1109/SOCPAR.2010.5686403","url":null,"abstract":"The governing behaviors of individuals in crowded places offer unique and difficult challenges, and limit the scope of conventional surveillance systems. In this paper, we investigate the crowd behaviors and localize the anomalies due to individual's abrupt dissipation. The novelty of the proposed approach can be described in three aspects. First, we introduce block-clips by sectioning the video segments into non-overlapping spatio-temporal patches to marginalize the arbitrarily complicated and dense flow field. Second, we treat the flow field in each block-clip as 2d distribution of samples and mixtures of Gaussian is used to parameterize it keeping generality of flow field intact. K-means algorithm is employed to initialize the mixture model and is followed by Expectation Maximization for optimization. These mixtures of Gaussian result in the distinct flow patterns precisely a sequence of dynamic patterns for each block-clip. Third, a bank of Conditional Random Field model is employed one for each block clip and is learned from the sequence of dynamic patterns and classifies each block-clip as normal and abnormal. We conduct experiment on two challenging benchmark crowd datasets PETS 2009 and University of Minnesota and results show that our method achieves higher recognition rates in detecting specific and overall crowd behaviors. In addition, the proposed approach shows dominating performance during the comparative analysis with similar approaches in crowd behavior detection.","PeriodicalId":397384,"journal":{"name":"2010 International Conference of Soft Computing and Pattern Recognition","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130396602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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