Authors:
Pranav Mantini
and
Shishir K. Shah
Affiliation:
University of Houston, United States
Keyword(s):
Camera Placement Optimization, Human Motion Forecasting, Occupancy Map Estimation, Face Detection, Human Activity, Effective Surveillance.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Camera Networks and Vision
;
Computer Vision, Visualization and Computer Graphics
;
Motion, Tracking and Stereo Vision
;
Video Surveillance and Event Detection
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.
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