# A new Video Synopsis Based Approach Using Stereo Camera

arXiv: https://arxiv.org/abs/2106.12362 · HTML: https://mlsanity.com/paper/2106.12362/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 46 references that are in the map.
- First posted on arXiv 23 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Classification-based Financial Markets Prediction using Deep Neural Networks](https://mlsanity.com/paper/1603.08604/) (2016)
- [Diversity-aware Multi-Video Summarization](https://mlsanity.com/paper/1706.03123/) (2017)
- [Regression-based Online Anomaly Detection for Smart Grid Data](https://mlsanity.com/paper/1606.05781/) (2016)

## Built on by (most cited first, 1 of 1)

- [Manipulating a Tetris-Inspired 3D Video Representation](https://mlsanity.com/paper/2407.08885/) (2024)

## Abstract (arXiv)

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.