Camera Placement Optimization Conditioned on Human Behavior and 3D Geometry

Abstract

This paper proposes an algorithm to optimize the placement of surveillance cameras in a 3D infrastructure. The key differentiating feature in the algorithm design is the incorporation of human behavior within the infrastructure for optimization. Infrastructures depending on their geometries may exhibit regions with dominant human activity. In the absence of observations, this paper presents a method to predict this human behavior and identify such regions to deploy an effective surveillance scenario. Domain knowledge regarding the infrastructure was used to predict the possible human motion trajectories in the infrastructure. These trajectories were used to identify areas with dominant human activity. Furthermore, a metric that quantifies the position and orientation of a camera based on the observable space, activity in the space, pose of objects of interest within the activity, and their image resolution in camera view was defined for optimization. This method was compared with the state-of-the-art algorithms and the results are shown with respect to amount of observable space, human activity, and face detection rate per camera in a configuration of cameras.

Optimal Camera Placement To Visualize Surrounding View From Heavy Machinery

“…The Optimal Camera Placement (OCP) problem has been thoroughly studied over the past decade, particularly for surveillance applications, [24,26]. Apart from surveillance, OCP problem has found interest in various applications such as, wireless sensor network deployment, [4,46], industrial monitoring, [47], motion capture systems, [38], human behaviour analysis, [28], agriculture, [12], and even online gaming, [2], to name a few. Some literature [41] classifies the OCP problem into categories such as target-based, area-based and probing-based coverage models.…”

A Multiresolution Approach for Large Real-World Camera Placement Optimization Problems

“…Camera placement optimization or optimal camera placement (OCP) is a decades old problem. OCP problems have been applied to a wide range of applications such as, video surveillance, [4], 3D reconstruction of objects, [5], human behaviour monitoring and motion capture systems, [6], [7], OCP with VR interface, [8], and so on. Problem formulation frameworks range from simple single objective, minimization or maximization problems, [9], to more complex, multiobjective or non-linear problems, [10].…”