Abstract
Violence is an extreme activity that presents a clear danger to human lives, human properties and governing authorities . Violence emancipates from strong protests to extreme disturbing activities by the mob. Violence unfolds itself from low level violence to extreme violence. This journey has two major steps. Stone Pelting and Arson activities are the two most ferocious activities by mobs from which classify the violence as building from low level violence to extreme violence. Arson is a spontaneous activity which is executed by protestors for showing extreme emotional dissent to the governing authorities. Lead by mob, arson is a dangerous activity and constitute most ferocious form of violence. Stone pelting is again an extreme case of mob fury against governing authorities. Pelting refers to throwing number of things at someone or something very quickly. Stone pelting is thus the most feared form of crowd violence that needs to be tackled on priority. Arson and stone pelting activities generate a fear in crowd and endangers life of humans and public and private property. The paper presents an application oriented deep learning framework using transfer learning approach for identification of arson and stone pelting in the images and videos. Cities which are classified as sensitive can opt for arson and stone pelting identification scheme for the protection of people and properties. We present a 2D ConvNets based transfer learning model for classifying extreme violence of arson and stone pelting. For a proof of concept, a small dataset is curated containing arson images, stone pelting and normal images.
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Tripathi, G., Singh, K., Vishwakarma, D.K. (2021). Detecting Arson and Stone Pelting in Extreme Violence: A Deep Learning Based Identification Approach. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_44
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