Vehicle Classification

With rapidly growing affordable sensors such as closed‐circuit television (CCTV) cameras, light detection and ranging (LiDAR), and even thermal imaging devices, vehicles can be detected, tracked and categorized in multiple lanes simultaneously. The accuracy of vehicle classification can be improved by combining multiple sensors such as thermal imaging, LiDAR imaging, and RGB visible cameras.

Traffic Flow Analysis

Analysis of traffic flow has been studied extensively for intelligent transportation systems using both invasive methods and non-invasive methods such as cameras.

Parking Occupancy Detection

Visual parking space monitoring is used with the goal of parking lot occupancy detection. Computer vision applications power decentralized and efficient solutions for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN).

Automated License Plate Recognition

Many modern transportations and public safety systems depend on the ability to recognize and extract license plate information from still images or videos.

Vehicle re-identification

With improvements in person re-identification, smart transportation and surveillance systems aim to replicate this approach for vehicles using vision-based vehicle re-identification.

Pedestrian Detection

The detection of pedestrians is crucial to intelligent transportation systems,  it ranges from autonomous driving to infrastructure surveillance, traffic management, transit safety and efficiency, and law enforcement.

Traffic Sign Detection

Computer Vision applications are used for traffic sign detection and recognition. Vision techniques are applied to segment traffic signs from different traffic scenes and employ deep learning algorithms for the recognition and classification of traffic signs.

Collision Avoidance Systems

Vehicle detection and lane detection form an integral part of most advanced driver assistance systems. Deep neural networks have been used recently to investigate deep learning and the use of it for autonomous collision avoidance systems.

Road Condition Monitoring

Applications for computer vision based defect detection and condition assessment are developed to monitor concrete and asphalt civil infrastructure. Pavement condition assessment provides information to make more cost-effective and consistent decisions regarding the management of pavement network.

Infrastructure Condition Assessment

To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. Systems for Computer Vision-based civil infrastructure inspection and monitoring are used to automatically convert image and video data into actionable information.

Driver/Pilot Attentiveness Detection

Distracted driving and flying – such as daydreaming, cell phone usage and looking at something outside the car / plane – accounts for a large proportion of road traffic and aircraft fatalities worldwide. Artificial intelligence is used to understand behaviors, find solutions to mitigate air & road incidents.