Insights to Video Analytic Modelling Approach with Future Line of Research

Abstract

The area of Video analytics has made some significant improvement due to advancement in image processing and datamining techniques. However, the inclination is still towards image contents and less to mined contents owing to many unsolved issues. Although concept of mining is more than 2 decade old, but mining approaches are yet to be standardized in the area of video surveillance system. With evolution of newer set of challenges in video capturing, existing mining models finds itself less applicable due to unstructured format of dynamic frames. Hence, this paper discusses about video analytics and presents a brief discussion of frequently used mining approaches in video as well as discussed some recent studies in this direction in order to scale the degree of effectiveness in existing system. The paper also presents research gap and provided solution as future line of research as a possible way to overcome the research gap.

Framework for Contextual Outlier Identification using Multivariate Analysis approach and Unsupervised Learning

“…The proposed study is a continuation of our prior work [42], [43] where the present solution targets to develop a framework for video surveillance system that is capable of identifying outliers for a given set of captured video frames. The primary consideration of this paper is that existence of outliers is never instantaneous and normally exists on the given scene considering both time factor and spatial factor where both these factors can also be stated as contextual factors.…”

Simplified Video Surveillance Framework for Dynamic Object Detection under Challenging Environment

“…This section discusses about the existing research works that carried in the division of video data analytics. The previous works of Madhu Chandra and Reddy presented a) conceptual as well as research overview on video analytical modeling [11], b) an analytical framework [12] to fulfill the research gap found in [11] by using dictionary based approach and unsupervised learning (UsL) approach and c) in [13] multivariate analysis and UsL is used to identify the contextual outlier.…”