Notice of Violation of IEEE Publication Principles - Dynamic event detection in crowded environment by HOS
Abraham Mathew, Savitha V. Nair, B. Vinoth · 2016
Notice of Violation of IEEE Publication Principles"Dynamic Event Detection in Crowded Environment by HOS"by Abraham Mathew, Savitha V. Nair, B. Vinothin the Proceedings of the International Conference on Electrical, Electronics, and Optimization Techniques, March 2016, pp. 3642-3644After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.This paper is a duplication of the original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:"Swarm Intelligence for Detecting Interesting Events in Crowded Environments"by Vagia Kaltsa, Alexia Briassouli, Ioannis Kompatsiaris, Leontios Hadjileontiadis, and Michael G. Strintzisin the IEEE Transactions on Image Processing, Volume 24, Issue 7, March 2015, pp. 2153-2166For traditional computer vision methods the analysis of motion and behaviours in crowded scenes constitutes a challenging task, as barriers like occlusions, varying crowd densities and complex stochastic nature of their motions are difficult to overcome. As it has to be kept within reasonable limits, the one more complicating factor is the computational cost. It is very crucial to analyse crowded scenes in real time, or at least fast as possible, in many practical situations, considering the fact that security personnel should act quickly if something seems to be “unusual”. Anomalous is a problem which is not fitting into a familiar type, classification or pattern. HOS (Histogram of Oriented Swarm) is used for detecting and localizing anomalous events in videos of crowded scenes. HOS together with the HOG (Histogram of Oriented Gradient) are combined to give a descriptor that helps to effectively characterize each scene. The occurrences of gradient orientation in localized portion of images can be count by this technique. The HOS descriptor analyses and localizes the anomalous and normal events separately.