Scene Detection Sliding Time Filtering for Wireless Devices

The patent segments the scene detection process into two distinct phases: an offline learning phase where complex algorithms process training data to build classification models, and an online detection phase where the pre-trained model performs simple real-time classification. This segmentation allows complex computation to be performed beforehand, simplifying the device requirements during actual operation while maintaining high detection accuracy.

The patent performs preliminary action by conducting the learning and training process before deployment. The complex classification algorithms are trained offline using labeled scene data to create pre-computed decision models. These pre-trained models are then deployed to the device, eliminating the need for real-time learning phases and reducing the computational burden during actual scene detection operations.

The patent segments memory usage into offline training data storage and online model storage. The complex training datasets can be stored temporarily during offline processing, while the final deployed model requires minimal memory. This segmentation allows the system to achieve high accuracy without permanently storing large amounts of data on the device.

The patent creates a simplified copy of the complex classification model that can be deployed to resource-constrained devices. Instead of copying the entire training dataset and complex algorithms to the device, the system generates a compact representation or decision tree from the training process that captures the essential classification logic, significantly reducing memory requirements while preserving detection accuracy.