Multimodal drunk density estimation for safety assessment

Abstract

Drinking alcohol in excess leads to lower selfconsciousness, damaging a persons judgment and thus enhances risk of aggressive behavior. It leads to various problems like social abuse, violence, crime, and road accidents. Hence, density of drunk people in a given area is one of the indicators of safety risk. In this work we propose a novel framework to determine density of drunk people in a smart city scenario. Smart cities provide multiple sources of information such as audio, video, and text (online social networks). We detect presence of drunk persons along with time and location by analyzing these information sources individually and then fuse this information to obtain a single drunk index for a given location. We put special focus text analysis and propose a more accurate method to detect drunk event (person) with an accuracy of 84.2%. Experimental results demonstrate the functionality and efficacy of the proposed framework.