Video Data Extraction for Machine Learning Product Detection

The patent uses video frames as copies of real-world product images. By capturing video of products in various conditions and extracting frames, the system creates training data that replicates diverse imaging scenarios without requiring manual collection of hundreds of individual images. This copying approach maintains detection accuracy while significantly reducing the time and effort needed to assemble training datasets.

The system performs preliminary action by pre-capturing video footage of products under various lighting conditions, angles, and backgrounds before the training process begins. This advance preparation creates a ready pool of training images that can be quickly extracted and used, eliminating the need to collect images on-demand during the training process and thereby reducing overall training time.

The video capture system serves multiple functions simultaneously: it captures product images from various angles, records different lighting conditions, and documents diverse backgrounds all in a single recording session. This multi-functional approach replaces the need for separate data collection processes for each imaging condition, significantly simplifying the overall data collection complexity while maintaining high training data quality.

The patent merges multiple data collection requirements into a single video recording process. Instead of separately capturing images for different angles, lighting conditions, and backgrounds, the system combines all these variations into one continuous video recording, which is then processed to extract diverse training images. This merging approach reduces the complexity of coordinating multiple data collection operations.

The patent applies segmentation by dividing the video data into individual frames that can be processed independently. Each frame is extracted and evaluated to determine its suitability as training data, allowing the system to process video content in manageable units. This segmentation approach maintains high data collection efficiency while making the processing complexity manageable through systematic frame-by-frame analysis.

The system extracts useful training images from the video stream by identifying and isolating frames that meet specific quality criteria. This extraction process separates valuable training data from the continuous video flow, automatically filtering out unnecessary frames. The extraction approach maintains productivity by efficiently converting video data into usable training images while managing processing complexity through automated frame selection algorithms.