3. Deep learning in the video industry
Video has complex time and space information such as moving images, text, audio, and user viewing logs. It is easy to drive viewers' emotions. It is the most abundant media and will become the mainstream of information expression and communication in the future. However, most of the videos currently have some problems: First, as the scale of video formation increases, the task of video processing is more arduous, and the speed of information dissemination cannot keep up with the speed of the generation. For example, in the case of iQiyi, there are hundreds of thousands of video uploads every day. If these videos are reviewed and marked by manual methods, it will cost a lot of manpower and is inefficient. On the other hand, the content in the video cannot be used effectively. Although the video website has accumulated a large number of users, the gold traffic is difficult to realize in size. The application of big data and deep learning can analyze the information in the video sequence to achieve the purpose of understanding the video content, and provides a new perspective for solving the above problems.
3.1. Intelligent processing of video
It is a magic weapon to improve the efficiency of video production by analyzing the video picture content through big data and deep learning to realize the intelligent processing of video. Traditional TV stations are manual, editable, card-scheduled, and audited, which takes a long time and is inefficient. The application of deep learning will speed up the whole process: for the entire episode automatic card segment, for all semantic recognition, automatic extraction of letters, the review of the entire video becomes fully automatic. Each video attracts the user's clicks by description and screenshots. How to automatically select the most suitable screenshots in a huge amount of video every day, it is a problem that needs to be solved in the video field. Previously, the energy algorithm was used to select the picture with the biggest change or the fidelity as the screenshot. Nowadays, the video recognition and face recognition are integrated, and the screenshot of this video will be more appropriate.
Video uploading requires a rigorous yellow and violent detection, and artificial intelligence can save a lot of manpower. In the March 2016 national “anti-vice and pornography†campaign, it is an important and arduous task to review a large number of video image data on cloud storage platforms such as Baidu cloud disk, micro disk and 360 cloud disk. Manually review yellow, violence and other information. It will be very time consuming and manpower. Through the deep data learning technology based on deep learning, you can accurately identify illegal images or videos such as pornography, horror, and small advertisements on these platforms, which can help the developer team to reduce operational risks and legal risks, and save a lot of auditing manpower. For example, Tuptech is based on deep learning image recognition technology, launched image recognition cloud services, providing enterprises with a variety of image and video review, value-added, search services.
Thunder access to Tupu Technology's image recognition cloud platform, more than 98% of pornographic video is filtered by the machine, the review is less than 2% of the total, saving more than 98% of labor costs. Viscovery Creative Qingqing can monitor illegal content such as pornography, violence, anti-terrorism, etc. It can be used in the fields of webcasting and pirated content monitoring, saving 95% of manpower and conducting efficient analysis.
3.2. Deep learning to open a new business model: video e-commerce and new advertising implants
Deep learning enables a more precise match between advertising and customer needs in video big data. At present, the huge video big data resources have attracted top video websites including BAT. Ali and Youku Tudou bought and watched, Baidu and iQiyi's follow-up purchase, as well as Tencent video, Sohu video, and Mango TV all began to insert advertisements in the video screen. Automatically analyze the content of the video in the video through big data mining, and automatically generate information, labels, products and other content in the video. On the one hand, it can increase the click rate and sales of the products, on the other hand, it can achieve more accurate accurate matching of advertisements. Increase advertising, and ultimately achieve the goal of converting traffic to revenue. At the same time, it is also possible to monitor the effectiveness of the advertisement and obtain the number and duration of brand exposures in the video.
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