Localization of region of interest in surveillance scene

Abstract

In this paper, we present a method for autonomously detecting and extracting region(s)-of-interest (ROI) from surveillance videos using trajectorybased analysis. Our approach, localizes ROI in a stochastic manner using correlated probability density functions that model motion dynamics of multiple moving targets. The motion dynamics model is built by analyzing trajectories of multiple moving targets and associating importance to regions in the scene. The importance of each region is estimated as a function of the total time spent by multiple targets, their instantaneous velocity and direction of movement whilst passing through that region. We systematically validate our model and benchmark our technique against competing baselines through extensive experimentation using public datasets such as CAVIAR, ViSOR, and CUHK as well as a scenario-specific in-house surveillance dataset. Results obtained have demonstrated the superiority of the proposed technique against a few popular existing state-of-the-art techniques.

Video synopsis generation using spatio-temporal groups

“…It is visually less confusing too. group the trajectories based on various other criteria such as interest area based [18]- [20], movement graph based [21], by supervised or unsupervised machine learning [22], [23], or by using deep learning to understand the activities based on region(s) of interest [18].…”

An Efficient Sign Language Recognition (SLR) System Using Camshift Tracker and Hidden Markov Model (HMM)

“…Also, they have used fingertip positions detected by the depth camera SN Computer Science for the discriminative module to estimate hand pose using part-based pose retrieval strategy. Region-of-interest (ROI) detection and extraction from videos is discussed in [2], where the authors have used correlated probability density functions to model motion dynamics of multiple moving targets.…”