  In today's world, the amount of data produced in every field has increased at
an unexpected level. In the face of increasing data, the importance of data
processing has increased remarkably. Our resource topic is on the processing of
video data, which has an important place in increasing data, and the production
of summary videos. Within the scope of this resource, a new method for anomaly
detection with object-based unsupervised learning has been developed while
creating a video summary. By using this method, the video data is processed as
pixels and the result is produced as a video segment. The process flow can be
briefly summarized as follows. Objects on the video are detected according to
their type, and then they are tracked. Then, the tracking history data of the
objects are processed, and the classifier is trained with the object type.
Thanks to this classifier, anomaly behavior of objects is detected. Video
segments are determined by processing video moments containing anomaly
behaviors. The video summary is created by extracting the detected video
segments from the original video and combining them. The model we developed has
been tested and verified separately for single camera and dual camera systems.
