Analytics-Driven Solutions

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 situational awareness.

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 intersection control.
• 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 safety improvements.

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.

Proven Solution

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.