A method comprising: receiving sampled video information captured by a video capture device; generating first object detection information based on a cloud model and the sampled video information, the cloud model configured to detect objects within the sampled video information and included in a cloud computing environment connected to the video capture device, wherein the first object detection information includes a first plurality of bounding representations detected in a first image frame; generating second object detection information based on a first edge model and the sampled video information, the first edge model configured to detect objects within the sampled video information and employed at an edge device coupled with the video capture device, wherein the second object detection information includes a second plurality of bounding representations detected in the first image frame; selecting , based on comparing the first object detection information to the second object detection information, a plurality of training images from the sampled video information, wherein selecting the plurality of training images from the sampled video information comprises: selecting the first image frame for the plurality of training images based on a count of the first plurality of bounding representations not equaling a count of the second plurality of bounding representations; detecting motion information corresponding to motion of one or more detected objects within the plurality of training images; generating a plurality of annotated images based at least in part on comparing the first object detection information to the motion information; and generating a second edge model based upon training the first edge model using the plurality of annotated images. receiving sampled video information captured by a video capture device; generating first object detection information based on a cloud model and the sampled video information, the cloud model configured to detect objects within the sampled video information and included in a cloud computing environment connected to the video capture device, wherein the first object detection information includes a first plurality of bounding representations detected in a first image frame; generating second object detection information based on a first edge model and the sampled video information, the first edge model configured to detect objects within the sampled video information and employed at an edge device coupled with the video capture device, wherein the second object detection information includes a second plurality of bounding representations detected in the first image frame; selecting , based on comparing the first object detection information to the second object detection information, a plurality of training images from the sampled video information, wherein selecting the plurality of training images from the sampled video information comprises: selecting the first image frame for the plurality of training images based on a count of the first plurality of bounding representations not equaling a count of the second plurality of bounding representations; detecting motion information corresponding to motion of one or more detected objects within the plurality of training images; generating a plurality of annotated images based at least in part on comparing the first object detection information to the motion information; and generating a second edge model based upon training the first edge model using the plurality of annotated images. <EOS>