Our Data Science team gathered the most state-of-the-art AI skills, applied to image processing and video analysis, in order to capitalize on existing road traffic cameras
We have strong partnerships with main AI leaders:
Cyclope.ai has developed so for key technological bricks, which are then assembled to build robust and industrialized integrated software products.
Tracking
To follow a given vehicle or object from one frame to another
Lane segmentation
To pre-select and identify different areas within an image
Shape recognition
To identify and recognize determined shaep in an image
Classification
To distribute these objects into specific categories
Face detection
To count the number of occupants while systematically blurring faces.
Quality evaluation
To identify the images most likely to guarantee the detection
Platematching
To match license plates from their unique fingerprint
Anonymization
To blur all visible personal data and ensure privacy
By pushing further the limits of image processing, AI has enabled the disruptive emergence of the Computer Vision field.
These cutting-edge technologies make it possible to leverage cameras like never before, whether they are new or already installed.
Thanks to the AI algorithms learning capacity, our technologies always have room for performance improvement.
Already trained on millions of images, our algorithms are now able to provide an extremely fine understanding capacity.
We use specific optimization methods on calculation machines to run AI at-scale and make the computing architecture always more fluid.
By adapting each time to the specific needs of the project, our highly-skilled Solution Architects Team makes possible a proper and smooth integration into existing systems and tools.
Cyclope.ai regularly contributes to the advancement of Scientific and Industrial Research on an international scale, through scientific publications :
HyperFDA: A Bi-level optimization approach to Neural Architecture Search and Hyperparameters’ Optimization via Fractal Decomposition-based Algorithm
Accepted in the GECCO - Genetic and Evolutionary Computation Conference 2020
Real time Automatic Urban Traffic Management Framework Based on Convolutional Neural Network under Limited Resources Constraint
Accepted in the 17th ICIAR - International Conference on Image Analysis and Recognition 2020
Application guided Image Quality Estimation based on Classification
Accepted in 26th IEEE ICIP - International Conference on Image Processing, 2019