Advances in artificial intelligence mean applications increasingly can take on image recognition capabilities that allow them to identify objects, detect the age of human faces and screen out adult content. The Department of Homeland Security has worked for several years to implement a biometric monitoring system to verify travelers in U.S. airports, and they recently found success with a Customs and Border Protection pilot.
The system uses facial recognition software to compare photos of passengers against a database, allowing DHS officials to identify travelers who have overstayed visas or are wanted in criminal investigations.
These developments underscore the need for the government to remain abreast of ways to manage complex technology and maintain standards of living. Image recognition software has real-world implications for local governments and can help officials efficiently integrate and manage assets.
Image recognition software can now process an incredible number of images at unprecedented speeds, all using completely serverless technology — a staple of advanced image recognition APIs.
Moreover, image recognition software can integrate with other APIs to trigger various actions. When a camera senses a certain object, for example, it can fire off a text message or use a content moderation API to filter the image. And it can do all this while data is streaming from source to endpoint.
This integration potential allows us to build smarter systems. For instance, developers could pair image analysis with a weather application. A combination of actual event data with nearby and related information can add to the value of the event streams, accurately predicting and measuring the effect of recognized patterns on residents and businesses. The best part? Image recognition software becomes more accurate over time.
Technology has evolved beyond simple pattern recognition thanks to machine learning. Rather than merely identify similarities and differences among a series of images — like the characters “3,” “8” and “B” — applications can use machine learning to perform more complex analyses.
Computers can learn to draw their own conclusions about images or videos, tagging them accordingly. The more times a computer performs this sort of analysis, the more capable it becomes of correctly identifying and tagging other images in the future. Larger data sets lead to more accurate results, and feedback loops help eliminate errors.
The New York City Department of Transportation has partnered with IntelliScape.io to use image recognition to better understand major traffic events. A combination of image recognition and machine learning enables the system to detect traffic jams, weather patterns, parking violations and more, sending real-time alerts to city officials along the way.
This sort of collaboration represents a fraction of what’s possible with image recognition technology. By using the technology to more efficiently address the problems of today, cities can be prepared for the world of tomorrow.
Image recognition software will be the engine driving smart cities, though government officials might feel overwhelmed by the incredible potential. Here are three great places to start:
1. Expanding the ecosystem of connected devices. Most of the physical world we interact with is not connected to the Internet of Things. Machine vision is poised to change that, with image recognition software making it possible for applications to collect data from just about any object.
To capitalize on this capability, data-streaming infrastructure must become more sophisticated. Image recognition APIs allow developers to deploy the system directly within a network, allowing cities to process image information while data is in motion. The network can then deliver the processed data to its endpoint in real time.
2. Optimizing real-world infrastructure. Cities will be able to use image recognition to better understand how people and things move. With a wealth of data at their disposal, urban planners and city officials can make more effective decisions about where to improve physical infrastructure for optimal safety and efficiency.
Eventually, applications equipped with image recognition technology will be able to make decisions without humans. Visitors to the recent TechCrunch Disrupt Hackathon received a sneak peek at this capability when the Auto-Trash team revealed its smart garbage can. It uses a camera to detect items placed inside its lid and sort them accordingly.
3. Improving conservation efforts. Instead of dispatching a fleet of human workers to analyze and count trees, for example, city officials could use drones and image recognition software to monitor the count, color and health of a natural area. WildTrack, a nonprofit, has already developed the Footprint Identification Technique to identify endangered species using only images of their footprints.
Once a system like WildTrack has collected a wealth of data, civic leaders can use the information to coordinate responses when plants or wildlife are threatened.
Despite common concerns of Big Brother watching our every movement, image recognition software is still far from ubiquitous. The technology’s myriad use cases make it impossible to ignore, and cities around the world are set to capitalize on its incredible value. For government officials and citizens alike, the spread of image recognition software is certain to be a godsend.
Joe Hanson is a content strategist at PubNub, a groundbreaking data stream network for mobile applications. PubNub enables customers to build, scale and manage real-time functionality for Web and mobile applications and IoT devices.