  We present a scalable framework designed to craft efficient lightweight
models for video object detection utilizing self-training and knowledge
distillation techniques. We scrutinize methodologies for the ideal selection of
training images from video streams and the efficacy of model sharing across
numerous cameras. By advocating for a camera clustering methodology, we aim to
diminish the requisite number of models for training while augmenting the
distillation dataset. The findings affirm that proper camera clustering notably
amplifies the accuracy of distilled models, eclipsing the methodologies that
employ distinct models for each camera or a universal model trained on the
aggregate camera data.
