Computer vision, a pivotal aspect of technological advancement, is broadly categorized into two types of applications: process automation (both online and offline) and the development of innovative products and services, such as autonomous vehicles and IoT solutions.
Offline Computer Vision Applications
Simplifying E-mobility with Computer Vision
E-mobility and, more specifically, E-Scooter-sharing is an industry that has been rapidly growing over the last couple of years. Computer Vision has been helping these kinds of companies ensure that their clients park their scooters correctly. If we take the example of CityScoot, Computer Vision helps them verify if a scooter is present in the picture to guarantee reliability. Plus, it reads the identification number or license plate and checks that the scooter is parked in a space authorized for two wheels.
Automated Checkout Systems
Computer vision transforms the billing process, creating smoother customer experiences in stores and restaurants by eliminating the need for barcodes. This technology enables instant product recognition and billing, as Compass Group demonstrated, effectively reducing queues in its corporate restaurants.
Advancing Animal Welfare with Computer Vision
Computer vision significantly enhances laboratory animal welfare monitoring. Focusing on nocturnal behavior analysis in mice, this technology detects activity, immobility, and eating or drinking patterns directly in their cages. Sanofi uses this automated approach, providing their researchers with graphical data and facilitating accurate study assessments and protocol adjustments while reducing stress-induced biases in behavior observation.
Online Computer Vision Applications
Envisioning the Marketplace of the Future
Marketplaces, especially in the second-hand sector, increasingly rely on computer vision for product sheet creation. This technology ensures photo consistency with product descriptions, enhancing site indexing and customer experience.
Creating Safer Social Networks
Computer vision aids in social network moderation by identifying inappropriate content, thus reducing the psychological burden on human moderators. Neural networks trained to recognize harmful content can filter it more efficiently.
Transforming Daily Objects into Intelligent Entities
A prime example of this transformation is the autonomous car. Using cameras, these self-driving vehicles navigate by interpreting their surroundings, promising to significantly lower accident rates.
The vast computer vision applications, from automated checkout systems to enhancing user experiences in social media and e-commerce, demonstrate its integral role in technological progress.