Patent ID: 11869212
Assignee: DEEPING SOURCE INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 18:
19. A method of training a video object detection model by using a training dataset, comprising steps of:
(a) in response to acquiring a training image from the training dataset, a learning device (i) inputting the training image and first prior information, set as having each of probabilities of each of objects existing in each of locations in the training image, into the video object detection model, to thereby instruct the video object detection model to detect the objects from the training image by referring to the first prior information and thus output first object detection information, and (ii) generating second prior information, which includes location information of the objects on the training image, by using the first object detection information;
(b) the learning device inputting the training image and the second prior information into the video object detection model, to thereby instruct the video object detection model to detect the objects on the training image by referring to the second prior information and thus output second object detection information;
(c) the learning device generating at least one loss by referring to the second object detection information and a ground truth corresponding to the training image, to thereby train the video object detection model to minimize the loss;
wherein, at the step of (a), the learning device generates, as the second prior information, a bounding box vector by vectorizing multiple pieces of location information of each of bounding boxes corresponding to each of the objects included in the first object detection information; and
wherein, at the step of (b), the learning device performs at least one of processes of (i) inputting the bounding box vector into the video object detection model, to thereby instruct the video object detection model to (i-1) apply predetermined pixel values, corresponding to the bounding box vector, to the training image by an addition, a multiplication, or a concatenation thereof, thus generating a modified training image, and (i-2) perform a part of a forward-propagation of the modified training image, and (ii) inputting the bounding box vector into the video object detection model, to thereby instruct the video object detection model to (ii-1) apply predetermined feature values, corresponding to the bounding box vector, to at least one feature map by an addition, a multiplication, or a concatenation thereof, thus generating at least one modified feature map, and (ii-2) perform the part of the forward-propagation of the modified feature map,
wherein the predetermined pixel values and the predetermined feature values correspond to multiple pieces of the location information of the bounding boxes corresponding to the bounding box vector, and wherein the feature map is obtained through the forward-propagation of the training image.