The client received a powerful AI-driven computer vision platform designed to ingest camera footage in order to detect, identify, and track faces, objects, and vehicles.
The solution effectively uses filtration and business logic, such as timeline dependencies, to reduce error in complex scenarios like partially obscured faces or vehicles moving in snowfall.
Oxagile’s team successfully implemented the evidence redaction feature that allows users to have any face or object of their choice blurred in every frame of the video. This process is critical for witness protection in court and was previously done by hand, which consumed a lot of time and increased the risk of human error.
At the ingestion stage, the video file is decoded and presented as a set of frames. Then, advanced pre-processing algorithms are used to fix the fish-eye distortion of body-worn cameras.
The solution relies on neural networks to find required entities in every frame, detect people’s poses, and locate the vehicles’ license plates. With all objects of interest discovered, the system is able to track them across a group of frames.
Finally, the system produces a report with extensive metadata regarding each entity and its appearance in every frame (e.g. a thumbnail of every detected vehicle, its license plate, color, body style, and behavior on the road).