Anomalous Object Detection with Deep Learning
L. Poojitha · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract: In many computers vision systems, object identification and monitoring are crucial characteristics. Object identification and monitoring is a difficult job in the fields of computer vision that attempts to identify, recognize and track things across a video series of pictures. It aids in the understanding and description of object behaviour rather than relying on human operators to monitor the computers. Its goal is to find moving things in a video clip or a security camera. On the other hand, rely heavily on computers, on the other hand many respect attributes approaches. The system collects a snapshot from the camera, process it as per the models requirements and the outputs the data to the tensorflow framework which provides a list of the frames discoveries and the objects dependability points and connections to their binding containers. Before exposing the object to the user, the software takes those connections and creates a rectangle adjacent to it. This research detects the presence of anomalous items in camera-captured sequences, with anomalies being things that correspond to categories that should not be present in a given scene.