| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_a68790a12228f5b99e176e8aacff640a |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10016
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20016
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30252 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-241
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-22
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-248
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-11
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-214
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-73
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-774
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-7715
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-246 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-26
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V20-50 |
| filingDate |
2022-01-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_3abd0947ccb3b106786bbf2a0fc91d5c
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_8ea434c1a4b4e36645f0311729f3790b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1b45ef4813af4fdc1d6fe4eb486526b7
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_bf331c16e22217cb91042bfe2b1345ff
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_bdef7d3fe75d77722e54db3ec6d2417d |
| publicationDate |
2022-07-14-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
DE-102022100360-A1 |
| titleOfInvention |
MACHINE LEARNING FRAMEWORK APPLIED IN A SEMI-SUPERVISED SETTING TO PERFORM INSTANCE TRACKING IN A SEQUENCE OF IMAGE FRAMES |
| abstract |
A method and system are provided for tracking instances within a sequence of video frames. The method includes the steps of processing an image frame through a backbone network to generate a set of feature maps, processing the set of feature maps by one or more prediction heads, and analyzing the embedding features corresponding to a set of instances in two or more image frames correspond to the sequence of video frames to establish a one-to-one correlation between instances in different image frames. The one or more prediction headers include an embedding header configured to generate a set of embedding features corresponding to one or more instances of an object identified in the image frame. The method may also include training the one or more prediction heads using a set of annotated image frames and/or multiple sequences of unmarked video frames. |
| priorityDate |
2021-01-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |