Information-Driven Video Communication for Public Safety Networks

Principal Investigator: 

Rui (April) Dai

Assistant Professor

Department of Electrical Engineering and 

Computing Systems

University of Cincinnati

Background

 Networked video surveillance systems have been used

heavily in today’s public safety infrastructure.

 More and more wireless cameras are deployed, and there

is an increasing demand of bandwidth for delivering

surveillance videos.

 Automatic video analysis tools, which attempt to detect,

recognize, track objects, and understand their behaviors,

have been applied in surveillance systems to provide realtime analysis results.

 Applying automatic video analysis at network edges in a

distributed manner can potentially

• alleviate the bandwidth pressure;

• enable better real-time response to users.

Objective

 Design an information-driven video communication

framework for wireless surveillance systems with the

objective to maximize the information that human

operators could gain with the help of automatic video

analysis.

Methodology

 Existing work

• Traditional communication protocols aim to satisfy QoS

(quality of service) or QoE (quality of experience)

requirements. However, QoS and QoE do not directly

reflect the amount of information that could be gained

from a wireless surveillance system.

 Proposed work

• Bridge the gap between the quality requirements from

public safety video users and the research in wireless

networking and distributed in-network data analysis.

• Quantify the quality of information for wireless

surveillance applications and design networking

solutions to maximize the information gain.

Research Plan

 Task 1: Building analytical quality of information (QoI) 

models for the visual information obtained from 

distributed automatic video analysis.

• Statistical analysis on a large amount of video traces

which align with common use cases of surveillance

video analytics.

• Explore features related to several sources of

information loss, including lossy video compression,

packet losses and delays, and the computational

limitations of local data processing.

• Establish relationships between QoI and the features.

Partner:

Department of Public Safety

University of Cincinnati

(Participants include law

enforcement officers and parking

services employees)

 Task 2: Designing communication protocols to support

efficient and real-time distributed in-network video

analysis.

• In a hierarchical clustered network, use intra-cluster and

inter-cluster communication to enable the collaboration

of cameras.

• Design channel access, scheduling, and routing

protocols for multiple cameras to extract the maximum

amount of information through in-network processing.

 Task 3: Designing an information-driven video streaming

application to effectively disseminate visual information

from surveillance cameras to human operators.

• Deliver bandwidth-consuming visual information,

including raw video and analysis results, to first

responders in real-time.

• Design and develop a scalable video streaming

application to adjust to the available bandwidth for

human operators connected to the surveillance network.

• Perform QoI-aware rate control to maximize the quality

of information delivered to human operators.

Major Milestones

 Task 1: Year 1 months 1-12 and Year 2 months 1-6.

• Month 3 of Year 1: Finish collecting raw video data and

gathering sample use cases from our partner.

• Month 3 of Year 2: Finish gathering QoI evaluation

results on our test datasets from our partner.

• Month 6 of Year 2: Complete the QoI models.

 Task 2: Year 2 months 1-12.

• Month 12 of Year 2: Complete the design and evaluation

of the proposed intra- and inter- cluster communication

protocols.

 Task 3: Year 3 months 1-12.

• Month 6 of Year 3: Finish the design and evaluation of

the proposed video streaming application.

• Month 12 of Year 3: Complete the joint evaluation of the

research components in Tasks 1-3.

Expected Impact

 Enable efficient and real-time distributed in-network video

analysis under bandwidth limitations for surveillance systems.

 Enhance the situational awareness of first responders.

 Increase the speed and precision of decision making for

various incidents in public safety.

 The software and evaluation platforms resulted from this

project will be made available for the research community for

further studies on video surveillance systems.

Video capture, video coding and transmission

Object Detection Classification

Understanding Tracking

Identification