Vehicle Track Detection Using Collision Cones to Cut False Alarms

The patent extracts and identifies the specific object responsible for collision risks from the video feed using object detection models. By isolating and tracking the responsible object (e.g., another vehicle, pedestrian, or obstacle), the system eliminates false alarms caused by unrelated movements in the scene, thereby reducing unnecessary emergency dispatches and resource consumption while maintaining accurate safety monitoring.

The patent introduces an object detection and tracking model as an intermediary between the video analytics software and the collision detection system. This intermediary layer processes video frames to identify and track specific objects, providing refined information to the collision detection algorithm. This mediator enables accurate identification of responsible objects, reducing false positives and optimizing resource allocation by distinguishing between actual threats and benign scene elements.

The patent segments the video analysis process into distinct functional modules: object detection model for identifying objects, tracking model for following object trajectories, and collision detection algorithm for assessing risks. Each module handles a specific aspect of the analysis, improving measurement precision through specialized processing while managing complexity through modular design that allows independent optimization and maintenance of each component.

The patent implements a multi-functional track detection system that performs object detection, tracking, and collision risk assessment within a unified framework. The object detection model serves multiple purposes: identifying objects for collision detection, providing data for tracking algorithms, and generating information for safety analytics. This universal approach consolidates multiple functions into a single system, improving precision across all tasks while avoiding the complexity of separate dedicated systems.