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1. WO2017131629 - MERGING OBJECT DETECTIONS USING GRAPHS

Note: Text based on automatic Optical Character Recognition processes. Please use the PDF version for legal matters

CLAIMS

What is claimed is:

1 . A method for merging object detections, comprising:

receiving a plurality of object detections, each object detection comprising an identifier;

detecting, via a processor, that a threshold number of object detections with a same identifier has been exceeded;

constructing, via the processor, a graph comprising at least one connected component, wherein each connected component comprises object detections with the same identifier that do not exceed a distance threshold between each other as vertices connected by edges; and merging, via the processor, the vertices in each connected component to generate a merged detection.

2. The method of claim 1 , wherein merging the vertices further comprises repeating the constructing of connected components and the merging of vertices within connected components until no more merging is detected.

3. The method of claim 1 , wherein merging the vertices further comprises computing a centroid of positions of the object detections and a mean size of the object detections to generate a single merged detection of the object over one or more iterations.

4. The method of claim 1 , wherein merging the vertices further comprises computing a weighted mean size, a weighted mean position, and an arithmetic mean confidence of object detections.

5. The method of claim 1 , further comprising merging the vertices representing the object detections if the object detections corresponding to the vertices also further have a size difference less than a threshold difference.

6. A system for noise reduction, comprising:

a graph builder to a receive a plurality of detections and build a graph based on the detections, each detection comprising a class ID corresponding to an object;

a connected component detector to detect connected components in the graph, wherein the connected components each represent that a threshold number of detections with a same class ID has been exceeded and that detections with the same class ID do not exceed a distance threshold between vertices that represent the detections; and

a merger to merge two detections with the same class ID in the graph based on the threshold distance to generate a merged detection.

7. The system of claim 6, wherein the merged detection comprises a weighted mean size, a weighted mean position, and an arithmetic mean confidence of detections represented through vertices within a connected component of the graph.

8. The system of claim 6, wherein the merged detection comprises a centroid of positions of the detections and a mean size of the object detections.

9. The system of claim 6, wherein the plurality of detections comprise a position, a size, and an identifier comprising the class ID.

10. A non-transitory, tangible computer-readable medium, comprising code to direct a processor to:

receive a plurality of detections, each detection comprising a class ID; build a graph based on the detections;

detect connected components in the graph, wherein the connected

components represent that a threshold number of detections with a same class ID has been exceeded and that detections with the

same class ID do not exceed a distance threshold between vertices that represent the detections;

merge detections with the same class ID in the graph based on the

threshold distance to generate a merged detection; construct a list of merged detections; and

display a list of merged detections in a visualization.

1 1 . The non-transitory, tangible computer-readable medium of claim 10, further comprising code to direct the processor to compute a weighted mean size, a weighted mean position, and an arithmetic mean confidence of the detections represented through the vertices within a connected component of the graph.

12. The non-transitory, tangible computer-readable medium of claim 10, further comprising code to direct the processor to compute a centroid of positions of the detections and a mean size of the detections to generate a merged detection.

13. The non-transitory, tangible computer-readable medium of claim 10, further comprising code to direct the processor to merge the detections if the detections also further have a size difference less than a threshold difference.

14. The non-transitory, tangible computer-readable medium of claim 10, further comprising code to direct the processor to detect an outlier detection that is not connected to more than a threshold number of detections with the same class ID and removing the outlier detection from the graph.

15. The non-transitory, tangible computer-readable medium of claim 10, further comprising code to direct the processor to calculate a similarity score and merge the detections in response to detecting that the similarity score exceeds a threshold similarity score.