System and method for training of a detector model to output an instance identifier indicating object consistency along the temporal axis

A detector system having a detector model, the detector system comprising:

• one or more processors;
• and
• a memory in communication with the one or more processors, the memory having:
• an image acquisition module, the image acquisition module having instructions that, when executed by the one or more processors, cause the one or more processors to obtain a first training set that includes images each having pixels that form one or more objects, the one or more objects being annotated with a known object location and a known class label,
• a training module, the training module having instructions that, when executed by the one or more processors, cause the one or more processors to train the detector model using the first training set and a first loss function, the first loss function expresses a difference between the known object location and the known class label for the one or more objects and a predicted object location and a predicted class label for the one or more objects as predicted by the detector model,
• a label propagating module, the label propagating module having instructions that, when executed by the one or more processors, cause the one or more processors to label propagate a second training set by the detector model after the detector model is trained with the first training set, the second training set includes images having pixels that form one or more objects, the images of the second training set are sequentially associated with at least one image of the first training set, and
• the training module further having instructions that, when executed by the one or more processors, cause the one or more processors to train the detector model using the first training set, the second training set, the first loss function, and a discriminative loss function, wherein the object detector model learns an instance identifier from the known object location of the one or more objects of the first training set and the second training set using the discriminative loss function, the instance identifier expressing a temporal consistency of the one or more objects along a temporal axis, wherein the detector model is trained through an intermediate multidimensional feature predicted at each pixel location of the one or more objects of the first training set and the second training set, the intermediate multidimensional feature being the instance identifier.

A non-transitory computer-readable medium storing instruction that, when executed by one or more processors, cause the one or more processors to:

• obtain a first training set that includes images having pixels that form one or more objects, the one or more objects being annotated with a known object location and a known class label;
• train a detector model using the first training set and a first loss function, the first loss function expresses a difference between the known object location and the known class label for the one or more objects and a predicted object location and a predicted class label for the one or more objects as predicted by the detector model;
• label propagate a second training set by the detector model after the detector model is trained with the first training set, the second training set includes images having pixels that form one or more objects, the images of the second training set are sequentially associated with at least one image of the first training set;
• and
• train the detector model using the first training set, the second training set, the first loss function, and a discriminative loss function, wherein the detector model learns an instance identifier from the known object location of the one or more objects of the first training set and the second training set using the discriminative loss function, the instance identifier expressing a temporal consistency of the one or more objects along a temporal axis, wherein the detector model is trained through an intermediate multidimensional feature predicted at each pixel location of the one or more objects of the first training set and the second training set, the intermediate multidimensional feature being the instance identifier.