The purpose of this thesis is threefold. First, DEtection TRansformer (DETR) network performance is evaluated against Single shot detector Resnet50 FPN (SSD) on an ensemble of common open source datasets. Based on this baseline work, a novel algorithm for detecting "interesting" events is developed and evaluated. It is based on the combination of two different object detection models trained on the same data. On one hand a standard object detection model trained to recognize 10 different labels. On the other hand, a smaller mobile friendly network trained to recognize if a given object is there or not, called No Label Network (NOLAN).