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LBP-Top Descriptor for Detecting Interesting Events in Crowded Environments


Affiliations
1 Dept of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, India
 

Objectives: To detect interesting events in crowded environments by introducing Local Binary Patterns from Three Orthogonal Planes (LBP-TOP).The mission of automatically detecting frames with anomalous or interesting events from long duration video sequences has become the research interest in the last decade.

Methods/Statistical analysis: The existing system introduced a Swarm Intelligence based approach for Detecting Interesting Events in Crowded Environments. In this system both appearance and motion are measured to detect the anomalies. The Histograms of Oriented Gradients (HOG) is used for capture the appearance information and Histograms of Oriented Swarms (HOS) is used for capture the frame dynamics. Both are combined to form a new descriptor that effectively characterizes each scene. However it does not considered dynamic texture to achieve high accuracy. To solve this problem the proposed system introduced histogram of LBP-TOP to represent dynamic texture.

Findings: In a time window of each frame average triplets of HOG, HOS and LBP-TOP are consecutively computed. Then, these features are passed as an input to classifier. Here proximal support machine is used for classification. Proximal Support Vector Machine is based on Support Vector Machine, it is simpler and faster than traditional Support Vector Machines algorithm, which is especially suitable for large amounts of data or image classification and operations.

Improvements/Applications: The experimental results show that the proposed system achieves better performance compared with existing system.


Keywords

Histograms of Oriented Gradients, Histograms of Oriented Swarms and Texture.
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  • Anil M. Cheriyadat and Richard J. Radke. Detecting Dominant Motions in Dense Crowds. IEEE Journal of special topics in signal processing. 2010; 2(4), 568-581.

  • VagiaKaltsa, Alexia Briassouli, IoannisKompatsiaris, Michael G. Strintzis, Swarm-based motion features for anomaly detection in crowds, 2014 IEEE International Conference on Image Processing (ICIP), 2014; 2353-2357.

  • Ravi Ranjan, G. Sahoo, A New clustering approach for anomaly intrusion detection. International Journal of Data Mining & Knowledge Management Process (IJDKP) March 2014, 4(2), 29-38.

  • Amin Karami, ManelGuerrero-Zapata, A fuzzy anomaly detection system based on hybrid PSO-Kmeans algorithm in content-centric networks Neurocomputing, Elsevier 2014, 149, Part C, pp. 1253-1269.

  • Ibrahim Aljarah, Simone A. Ludwig, MapReduce Intrusion Detection System based on a Particle Swarm Optimization Clustering Algorithm, IEEE Congress on Evolutionary Computation: Cansun, Mexico. 2013; June 20-23, 955-962.

  • SeyedMojtab, HosseiniBamakan, BehnamAmiric, MahboubehMirzabagheri,Yong Shia , A New Intrusion Detection Approach using PSO based MultipleCriteria Linear Programming, 3rd Conference on Information Technology and Quantitative Management (ITQM 2015). 2015; 55, 231-237.

  • FangjunKuang,Siyang Zhang, Zhong Jin, WeihongXu. A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection.2015; 19(5), 1187-1199.

  • Saber M. Elsayed, Ruhul A. Sarker, EfrénMezura-Montes. Self-adaptive mix of particle swarm methodologies forconstrained optimization, Elsevier, 2015; 277, 216-233.

  • K. K. Vardhini, T. Sitamahalakshmi. A Review on Nature-based Swarm Intelligence Optimization Techniques and its Current Research Directions. Indian Journal of Science and Technology, 2016; 9(10), 1-13.

  • A. A. Ayodele, K. A. Charles. Improved Constrained Portfolio Selection Model using Particle Swarm Optimization. Indian Journal of Science and Technology, 2015; 8(1), 1-8.


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  • LBP-Top Descriptor for Detecting Interesting Events in Crowded Environments

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Authors

M. Pavithra
Dept of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, India
M. Vimaladevi
Dept of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, India
M. Priyanga
Dept of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, India
S. Yamunadevi
Dept of Computer Science and Engineering, Hindusthan Institute of Technology, Coimbatore, Tamilnadu, India

Abstract


Objectives: To detect interesting events in crowded environments by introducing Local Binary Patterns from Three Orthogonal Planes (LBP-TOP).The mission of automatically detecting frames with anomalous or interesting events from long duration video sequences has become the research interest in the last decade.

Methods/Statistical analysis: The existing system introduced a Swarm Intelligence based approach for Detecting Interesting Events in Crowded Environments. In this system both appearance and motion are measured to detect the anomalies. The Histograms of Oriented Gradients (HOG) is used for capture the appearance information and Histograms of Oriented Swarms (HOS) is used for capture the frame dynamics. Both are combined to form a new descriptor that effectively characterizes each scene. However it does not considered dynamic texture to achieve high accuracy. To solve this problem the proposed system introduced histogram of LBP-TOP to represent dynamic texture.

Findings: In a time window of each frame average triplets of HOG, HOS and LBP-TOP are consecutively computed. Then, these features are passed as an input to classifier. Here proximal support machine is used for classification. Proximal Support Vector Machine is based on Support Vector Machine, it is simpler and faster than traditional Support Vector Machines algorithm, which is especially suitable for large amounts of data or image classification and operations.

Improvements/Applications: The experimental results show that the proposed system achieves better performance compared with existing system.


Keywords


Histograms of Oriented Gradients, Histograms of Oriented Swarms and Texture.

References