A framework for robust object multi-detection with a vote aggregation and a cascade filtering
This is an incremental improvement for product search applications in retail.
The paper tackles the problem of multi-object detection for product search on market shelves by introducing a framework that achieves a high detection rate with low false detection using only one pattern per object and no manual parameter adjustments.
This paper presents a framework designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. The framework uses a single feedback loop and a pattern resizing mechanism to demonstrate the top effectiveness of the state-of-the-art local features. A high detection rate with a low false detection chance can be achieved with use of only one pattern per object and no manual parameters adjustments. The method incorporates well known local features and a basic matching process to create a reliable voting space. Further steps comprise of metric transformations, graphical vote space representation, two-phase vote aggregation process and a cascade of verifying filters.