A new Video Synopsis Based Approach Using Stereo Camera

TL;DR

This paper introduces a novel video synopsis method that employs stereo cameras and object-based unsupervised learning to detect anomalies and generate concise summary videos by processing and analyzing object behaviors.

Contribution

The work presents a new approach combining stereo camera data with object tracking and anomaly detection for automatic video summarization.

Findings

Effective anomaly detection in video segments

Successful application to single and dual camera systems

Enhanced video summarization accuracy

Abstract

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.