Scene Change Localization in a Video

Millions of videos are uploaded each day to Youtube and similar platforms. One of the many issues that these services face is the extraction of useful metadata. There are a lot of tasks that arise with the processing of videos. For example, putting an ad is better in the middle of a video, and as an advertiser, one would probably prefer to show the ad in between scene cuts, where it would be less intrusive. Another example is when one would like to watch only through the most interesting or important pieces of video recording. In many cases, it is better to have an automatic scene cut detection approach instead of manually labeling thousands of videos. The scene change detection can help to analyze video-stream automatically: which characters appear in which scenes, how they interact and for how long, their relations and importance, and also to track many other issues. The potential solution can rely on different facts: objects appearance, contrast or intensity changed, other colorization, background chang, and also sound changes. In this work, we propose the method for effective scene change detection, which is based on thresholding, and also fade-in/fade-out scene analysis. It uses computer vision and image analysis approaches to identify the scene cuts. Experiments demonstrate the effectiveness of the proposed scene change detection approach.

Automatic Yarn Cut Detection in Power Loom using Iot

Textile trade has occupied second place next to agriculture. Due to the increase in population growth, textile trade in today's world is growing in plenty. A power loom is one of the main advances within weaving industrialization. It employs the country's more than thirty-five million people. Trade's main objective is to know its high-productivity power. The biggest downside which a textile trade is facing is that once the thread is cut the material gets broken. This results in the production of unnecessary cloth. The designed system is meant in such a simple way that it stops the device and avoids damage. This uses Raspberry pi 3 as the primary principal unit. The proposed system uses AC to DC Rectifier, A / D Converter and raspberry pi to signify automatic yarn cut detection in a loom. Using raspberry pi, this device mainly aims to detect the yarn cut in a loom. In this paper, the use of a single controller controls four power looms. Once the yarn cuts, the fault is detected, the supply may cut off immediately and then the fault is corrected.