US 12,217,501 B2
Identification apparatus, object identification method, learning apparatus, learning method, and recording medium
Hideaki Sato, Tokyo (JP); Katsumi Kikuchi, Tokyo (JP); and Kyota Higa, Tokyo (JP)
Assigned to NEC CORPORATION, Tokyo (JP)
Appl. No. 17/783,429
Filed by NEC Corporation, Tokyo (JP)
PCT Filed Dec. 24, 2019, PCT No. PCT/JP2019/050615
§ 371(c)(1), (2) Date Jun. 8, 2022,
PCT Pub. No. WO2021/130856, PCT Pub. Date Jul. 1, 2021.
Prior Publication US 2022/0392212 A1, Dec. 8, 2022
Int. Cl. G06V 20/17 (2022.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06V 10/774 (2022.01)
CPC G06V 20/17 (2022.01) [G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06V 10/774 (2022.01); G06T 2207/10032 (2013.01); G06T 2207/20081 (2013.01)] 16 Claims
OG exemplary drawing
 
1. An object identification apparatus comprising:
a first memory storing instructions; and
one or more first processors configured to execute the instructions to:
perform foreground extraction with respect to input images, and generate a foreground extraction result;
extract a state of a foreground of each input image based on the foreground extraction result, wherein the state includes a movement trajectory feature, an area variation feature, and an appearance feature of the foreground;
select one or more identification models based on the extracted state by using a selection model, wherein the selection model receives the state and outputs a likelihood to select each identification model, and wherein the selection model is trained using correct answer data having a value indicating the identification model to be used to identify a foreground of each of training input images; and
identify a moving object included in the input images by using the selected one or more identification models.