Virginia Patent of the Month – June 2024

ObjectVideo Labs, LLC , an advanced video software firm developing intelligent security video software solutions, has been granted a patent for their latest technology capable of enhancing object detection accuracy and significantly reducing false positives in camera-based systems. This innovative approach leverages advanced image processing and machine learning techniques to ensure that detection systems identify only relevant objects, minimizing errors caused by non-target objects such as trees or mailboxes.

The core of this method involves receiving a series of images from a camera and analyzing each one to identify detected objects, which are outlined by bounding boxes. By comparing these detected objects to ground truth labels, the system can pinpoint false detections. When the false detection count for a specific object surpasses a predefined threshold, an adversarial mask is generated for that object.

This adversarial mask is crafted based on pixel regions associated with the false detections, effectively targeting and modifying specific areas within the images. The goal is to alter these regions to prevent the detection system from misidentifying non-target objects as significant. This process is iterative, with the mask being continually refined through gradients and sign updates to optimize its effectiveness.

One of the standout features of ObjectVideo Labs’ method is its ability to adapt to various imaging conditions. The system iteratively applies the adversarial mask to new sets of images, adjusting and improving the mask’s parameters to ensure robustness across different lighting, weather, and seasonal conditions. This adaptability ensures that the mask remains effective without compromising the accuracy of true positive detections.

By implementing this advanced adversarial masking technique, ObjectVideo Labs’ method provides a powerful tool for enhancing the reliability of object detection systems. This technology not only improves detection accuracy but also reduces the need for manual intervention, making it a cost-effective and efficient solution for a wide range of applications, from security surveillance to autonomous vehicles.

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