Extracting insights from the video or using AI technology brings new challenges and provides considerable optimization compared to image processing. There is a misconception that video AI simply extracts specific frames from video material and runs computer vision algorithms on each video frame. Although this practice is indeed feasible, it does not really bring analytical insights. In today's article, we will use several examples to understand the shortcomings of this method of processing a single video frame. But given the limited space, I won't discuss in detail the other algorithms needed to overcome these shortcomings. Interested friends can refer to Video Indexer, which provides a variety of specific video algorithms that can achieve such goals. Let's take a look at the first 25 seconds in the following [video] Please note that Doug has been appearing in the picture for 25 seconds. If you want to draw a timeline for Doug's appearance in the video, it should look like the image below. Please note that during this process Doug is not fully lens oriented. In the 7th second of the video, he was staring at Emily - the same situation occurred in the 23rd second. If you run face detection within the corresponding time period in the video, Doug's face will not be detected (see screenshot below). In other words, if you only perform face detection on each video frame, you will not be able to draw the timeline shown above. To get such a timeline, we must be able to track the face across the video segment and consider the side view of the face that appears. The Video Indexer is able to track the face, which means you will be able to see the full timeline of the previous show. Please see the following two frames. The two frames are from the video of the presenter's speech on the stage, and the word "Microsoft" on the back wall has always been hidden. As a human viewer, we can of course easily infer that it is displaying "Microsoft." But if you run OCR on these two images, the output will only be "Microsc" and "crosoft". If you process a complete sequence of video frames in a video clip, you get a lot of this incomplete vocabulary. In order to extract the correct and complete vocabulary from the lens, you need to apply an algorithm to this part of the vocabulary. Video Indexer is able to do this and get better insights from the video. The face recognition system is composed of a face database, and the face database contains a set of training images pointing to different person objects. It also provides a query function for extracting facial features from the query image and matching it to the face database. The output of the query function contains a list of possible matches and a confidence value. The output quality of the query function will depend on the actual quality of the face database and the query image. In a video processing scenario, there will be multiple video frames, and the characters will appear with different head poses and lighting conditions. Of course, we can use the frame-by-frame processing method for face recognition system query when each character appears, but this method may lead to different face matching conclusions and confidence values ​​with huge differences between frames. In other words, we need to use an additional layer of logic to determine the face match results. As an optimization method, we can select a suitable subset of frames for targeted face recognition system queries, thereby reducing the actual number of queries for the system. While processing video, we can also build and enhance the face database by arranging trends using character training images from multiple video frames. In addition, you can also build logic to track cross-frame characters and use heuristic algorithms to evaluate changes. The Video Indexer can also do this, meaning users will be able to build higher quality face database results from the current video. Toolkits For Cutting Mahine and Cutting Materials
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