Precision Security: Integrating Video Surveillance with Surrounding Environment Changes

Wenfeng Wang,Guiwei Zhang,Hongwei Zheng,Boqian Wu,Peng Wei,Jing Qian,Xi Chen

doi:10.1155/2018/2959030

Abstract

Video surveillance plays a vital role in maintaining the social security although, until now, large uncertainty still exists in danger understanding and recognition, which can be partly attributed to intractable environment changes in the backgrounds. This article presents a brain-inspired computing of attention value of surrounding environment changes (EC) with a processes-based cognition model by introducing a ratio valueλof EC-implications within considered periods. Theoretical models for computation of warning level of EC-implications to the universal video recognition efficiency (quantified as time cost of implication-ratio variations fromλktoλk+1,k=1,2,…) are further established. Imbedding proposed models into the online algorithms is suggested as a future research priority towards precision security for critical applications and, furthermore, schemes for a practical implementation of such integration are also preliminarily discussed.

Highlights

Surveillance plays a vital role in maintaining social security and protecting infrastructure facilities of a country [1, 2]
There are still considerable uncertainties associated with danger understanding and recognition, especially for engineering-critical applications [3,4,5], which can be partly attributed to implications of environment conditions to video recognition efficiency of the surveillance system
It has been demonstrated that suitable model parameters in online algorithms and difficulty level of object detection tasks in different environments can be much different [6]

Summary

Introduction

Surveillance plays a vital role in maintaining social security and protecting infrastructure facilities of a country [1, 2]. How to evaluate and compute the regulated attention in implications of the surrounding environment changes and, how to define the warning level of EC-implications to video recognition efficiency should be research priorities towards precision security in intelligent surveillance [21,22,23,24,25,26,27]. Robustness and efficiency of some online algorithms in tackling special EC-implications in special scenario were validated in a series of previous studies until now, universal models for computation of the attention value and warning level of EC-implications to video recognition efficiency remain unaddressed and, an emergent issue is improving the current surveillance systems [16, 17]. To model brain cognition processes and establish theoretical models for precision computation of attention value of EC, and (3) to highlight necessity of introducing proposed models in critical applications

Preliminary Formulation

Theoretical Analyses

Simulation and Discussion