Selecting technology that is a sound and reliable investment
- By Joel White
- Oct 01, 2019
As city streets and sidewalks become progressively more
congested, city planners are facing new safety challenges,
including distracted pedestrians, a growing numbers of
bicyclists on the road and an increase in public transportation.
At the same time, smart and semi-autonomous vehicles that
can communicate with each other and with roadside infrastructure
are on the rise in cities and on highways.
The Right Technology
The challenge for senior traffic engineers and planners is complex.
From a broad spectrum of emerging technology coming from new
and well-established industry players, how do you choose the right
technology to improve mobility, safety and efficient use of roadways
now and into the future? How do you have a level of assurance that
the technology selected is a sound and reliable investment?
IP cameras with onboard analytics processing capability operating
as video sensors are a key component to intelligent transportation
systems that help to keep roadways flowing safely and efficiently.
In combination with other systems for city and traffic management,
smart IP cameras enable detection and monitoring solutions that
instantly alert the right people to safety risks and constantly gather
information on roadway usage to provide better insights and information
for data-driven decision-making.
As a result, city planners and senior traffic engineers can create a
smarter, safer and more sustainable transportation ecosystem.
Reliable Data and Detection
Video analytics technology can bring intelligence to infrastructure by
delivering solutions for traffic flow, improved safety, smart parking
and data collection. Analytics built in to IP cameras enable intelligent
video devices that can send safety risk alerts and deliver valuable data
for highway and urban infrastructure planning.
With built-in video analytics on IP cameras, analytics processing
is done at the edge—on the raw video stream at the camera sensor—
with no central analytics server required. The key advantages to builtin
analytics, as opposed to analytics that are added to the camera after
the fact or analytics processed downstream on a server, are the
camera’s architecture, image processing capability and overall system
efficiency that is designed from the ground up. Analyzing raw data at
the sensor also adds an increased level of precision because the raw
sensor data analyzed is uncompressed with no loss of video fidelity.
True analytics at the edge, in the camera, eliminate the need for
additional computers at the pole or expensive networks for cloudbased
analytics while still allowing continuous capture of high quality data. The result is a distributed network of cameras acting as intelligent
processing nodes with no single point of failure, delivering a
cost-effective and reliable video-as-a-sensor solution. Excellent video
images, intelligent event detection and alerts, and data collection and
aggregation can all come from the single video device.
With machine learning technology—the latest advancement in
video analytics—cameras can be taught to recognize user-defined
object classifiers. For example, machine learning can be used to accurately
count overlapping vehicles queued in front of traffic lights. In
many use cases, machine learning capability can improve detection
accuracy to enable precise vehicle counts with minimal errors.
Machine learning also enables customized solutions to solve the
specific needs of cities and transportation departments. For example,
it can detect the formation of icicles or snow buildup on bridges,
overpasses and tunnel openings, where they can pose a significant
safety risk to motorists or pedestrians passing underneath.
Enhance Safety with Video Analytics
Improve safety by alerting to risks on the road. Intelligent IP cameras
deliver automatic incident detection and verification for slow or
stopped vehicles, queues of vehicles at exit ramps, vehicles traveling
the wrong way, objects in the road—such as lost cargo—and other
traffic events. Alerts can be sent to traffic management centers or,
through integration with highway information solution providers,
the IP cameras can trigger third-party systems to notify drivers, improving
With video analytics:
- Early incident detection enables traffic and transportation operations
centers to implement the necessary workflows to resolve
road irregularities faster, enable first responders to intervene
more quickly, and avoid secondary accidents.
- Integration with dynamic message signs, and dedicated shortrange
communication (DSRC) broadcast messages to smart vehicles,
ensure drivers are instantly alerted to safety issues.
- Detection of pedestrians in a crosswalk can alert the traffic controller
to preempt traffic signals to increase safety, enabling smart
- Jaywalkers at night can trigger the IP camera to activate an onboard
illuminator to make pedestrians more visible to motorists.
- Integration with highway information solutions can broadcast messages
to smart vehicles to alert them to the presence of pedestrians
in the area the vehicle is travelling and display the pedestrian’s GPS
coordinates on a live map in the vehicle’s on board unit.
Analyze Data to Extend Beyond Safety
With video analytics, the IP camera becomes an intelligent sensor
that can classify objects as cars, trucks, bicycles, and pedestrians, and
detect speed and trajectory. Object classification enables the cameras
to recognize what they are viewing for data-gathering purposes.
Using video as a sensor and software tools for tapping into the
camera generated metadata, the data can be extracted and stored in
relational databases allowing city traffic planning directors and senior
traffic engineers to continuously collect real-time data to analyze flow
patterns on networks of roadways. The camera-generated data can
be used for implementing new policies that result in safer and more
efficient intersections. This data can also help them determine how
pedestrians use certain locations to provide insights into possible
Multiple cameras can feed data to dashboards that deliver actionable
information for business intelligence purposes—helping city
planners understand traffic patterns, congestion points, and more.
Data examples include:
- Pedestrian, bicycle, and vehicle counts
- Classification of vehicles, such as cars versus trucks
- Average speed and direction
- Road occupancy
Infrastructure owners can choose whether they want both video
and the data or just the data. When only data is needed, low bandwidth
connections can stream it from the sensor into the data warehouse,
reducing network impact and allowing video to stay private.
In busy cities, video analytics can also help monitor parking lot
occupancy and curbside parking as well as help enforce no-parking
zones. In lots, cameras can count the number of open parking spaces,
specialized spots—such as those for handicap or electric vehicles—or
track ingress and egress. The cameras then relay this data to the video
and parking management systems. Sharing this information, along
with alternative parking locations on a dynamic message sign, can
help drivers find open parking faster, getting them off the road and
reducing traffic congestion and emissions.
For smart parking applications, machine learning adds the advantage
of determining the amount of time a vehicle has been parked
and much more. For example, at the airport curbside for passenger
drop-off and pickup, vehicle parking time limit is restricted; machine
learning can detect and alert law enforcement to vehicles that exceed
the maximum time limit. Machine learning analytics technology
does not have a timeout dependency as compared to standard analytics
detections of parked vehicles.
While these types of capabilities and solutions may seem to be part
of futuristic solutions, smart connected city and transportation solutions
are already here and are being used in states and provinces
throughout North America, whether through full implementation or
in pilot projects.
For example, in one Midwestern state, IP cameras with built-in
video analytics are helping to improve highway safety by warning
drivers of dangers ahead. Some of these alerts include cross-traffic
warnings, curve-speed warnings, pedestrian detection, queue warnings,
work-zone warnings and wrong-way driver detection. Events
detected by the cameras trigger the highway information solution to
send messages about these incidents directly into the display units in
connected smart vehicles. For the majority of unconnected vehicles,
messages are broadcast to dynamic signs and other alert beacons.
From improving traffic flow on highways to implementing projects
for pedestrian safety initiatives and gathering data for smart infrastructure
planning, video sensor technology enables a wide range
of customized solutions to meet the needs of city planners and senior
traffic engineers around the world.
This article originally appeared in the October 2019 issue of Security Today.