This paper is published in Volume-3, Issue-3, 2017
Area
Electronics and Telecommunication Engineering
Author
Meenal Suryakant Vatsaraj
Co-authors
Prof. D. S Bade
Org/Univ
Alamuri Ratnamala Institute of Engineering and Technology (ARIET), Thane, Maharashtra, India
Pub. Date
25 May, 2017
Paper ID
V3I3-1380
Publisher
Keywords
Anomaly, Artificial Neural Network (Ann), Classifier, Computational Cost, Histogram of Oriented Gradients (Hog), Occlusion, Support Vector Machine (SVM), UCSD Dataset.

Citationsacebook

IEEE
Meenal Suryakant Vatsaraj, Prof. D. S Bade. Anomalous Behavior Detection in Crowded Environments Using Classifiers Artificial Neural Network and Support Vector Machine, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Meenal Suryakant Vatsaraj, Prof. D. S Bade (2017). Anomalous Behavior Detection in Crowded Environments Using Classifiers Artificial Neural Network and Support Vector Machine. International Journal of Advance Research, Ideas and Innovations in Technology, 3(3) www.IJARIIT.com.

MLA
Meenal Suryakant Vatsaraj, Prof. D. S Bade. "Anomalous Behavior Detection in Crowded Environments Using Classifiers Artificial Neural Network and Support Vector Machine." International Journal of Advance Research, Ideas and Innovations in Technology 3.3 (2017). www.IJARIIT.com.

Abstract

Our propose method focuses to detect and localize anomalous behavior in videos of crowded area means different scenario from dominant pattern. Proposed method consist motion and appearance information therefore different kinds of anomalies can be robustly identified in a wide range of situations. Histogram of oriented gradients can easily captures varying dynamic of crowded environment. Histogram of oriented gradients can also effectively recognize and characterize each frame of each scene. Our method of detecting anomalies using artificial neural network and support vector machine consist both appearance and motion features which extracts this features within spatio temporal domain of moving pixels that ensures robustness to local noise and thus increases accuracy in detection of local anomaly with low computational cost. UCSD dataset which will be used and which consist various situations with varying human crowds as well as traffic data with occlusions when feed to our propose method can achieve significantly higher accuracy probably more for pixel level events detection as compared to any other methods.
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