Google introduced the cloud API for object recognition on video
Example of the Cloud Video Intelligence API
Animal (97.76%)
Wild animal (92.16%)
Tiger (90.11%)
Land animal (68.17%)
Bengal Tiger (64.77%)
Whiskers (63.30%)
Zoo (58.16%)
Roaring cats (56,41%)
Cat (44.12%)
Google has announced the release of a new cloud service Cloud Video Intelligence API (now in the stage of closed beta testing). This is the first API on the market for video processing with object recognition.
Cloud Video Intelligence API is designed to process video content. Videos become indexed, with search by content. You can track all the appearances of the desired object, the duration of these appearances, and assess their importance. For example, automatically find all sex scenes in a multi-year archive from surveillance cameras. Searching videos is as easy as searching through text documents. You can upload videos to the service directly as needed, or by integrating with Google Cloud Storage .
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In-depth training systems, neural networks and AI are no longer a toy for scientific experiments, but a real technology that is increasingly used in business. In areas such as computer vision, speech recognition, natural language processing. Neural networks help to predict when degradation will occur in the network (analysis of geotagging tweets) what product the buyer wants to buy. Weak AI detects fraudulent transactions and analyzes medical images to help diagnose.
Google is one of the leaders in scientific research on the study of neural networks, and now this scientific leadership is gradually being converted into profit as commercial companies begin to use AI systems in their operations. From the point of view of the situation on the cloud market, experts say that services like object recognition in photos and videos are an attempt by Google to catch up to two leaders: Amazon and Microsoft. That is, Google is trying to use their intellectual superiority over competitors to get them.
Cloud Video Intelligence API recognizes not only nouns (tiger, animal, dog, flower), but also some verbs (run, swim, fly) in the contents of the video. It uses Google’s machine learning algorithms, created using TensorFlow and other frameworks.
There are two demo examples on the description page that show the capabilities of the system. In addition to assigning labels, as in the first screenshot, it automatically breaks the video into a sequence of scenes - and shows the labels for each scene. That is, the system recognizes scene changes.
The metadata from the Cloud Video Intelligence API video is retrieved by an easy-to-use REST API. The demo page again provides an example of such a request and response.
Annotation request
https://videointelligence.googleapis.com/v1beta1/videos:annotate?key=YOUR_API_KEY_HEREAnnotation request body
{ "inputUri": "gs://cloudmleap/video/next/animals.mp4", "features": [ "LABEL_DETECTION", "SHOT_CHANGE_DETECTION" ] } Reply with annotations
{ "name": "europe-west1.8668160167827035855" } Operation request
https://videointelligence.googleapis.com/v1/operations/europe-west1.8668160167827035855?key=YOUR_API_KEY_HEREAnswer
{ "name": "europe-west1.8668160167827035855", "metadata": { "@type": "type.googleapis.com/google.cloud.videointelligence.v1beta1.AnnotateVideoProgress", "annotationProgress": [ { "inputUri": "/cloudmleap/video/next/animals.mp4", "progressPercent": 100, "startTime": "2017-03-10T07:43:08.708173Z", "updateTime": "2017-03-10T07:43:16.478607Z" }, { "inputUri": "/cloudmleap/video/next/animals.mp4", "progressPercent": 100, "startTime": "2017-03-10T07:43:08.708173Z", "updateTime": "2017-03-10T07:43:17.493023Z" } ] }, "done": true, "response": { "@type": "type.googleapis.com/google.cloud.videointelligence.v1beta1.AnnotateVideoResponse", "annotationResults": [ { "inputUri": "/cloudmleap/video/next/animals.mp4", "labelAnnotations": [ { "description": "African elephant", "languageCode": "en-us", "locations": [ { "segment": { "startTimeOffset": "35201868", "endTimeOffset": "37537540" }, "confidence": 0.89221686, "level": "SHOT_LEVEL" } ] }, { "description": "Android", "languageCode": "en-us", "locations": [ { "segment": { "startTimeOffset": "67150410", "endTimeOffset": "69319294" }, "confidence": 0.7447677, "level": "SHOT_LEVEL" }, { "segment": { "startTimeOffset": "78078032", "endTimeOffset": "81706663" }, "confidence": 0.41145295, "level": "SHOT_LEVEL" } ] }, { "description": "Animal", "languageCode": "en-us", "locations": [ { "segment": { "startTimeOffset": "-1", "endTimeOffset": "-1" }, "confidence": 0.9850994, "level": "VIDEO_LEVEL" }, { "segment": { "startTimeOffset": "83458", "endTimeOffset": "1584934" }, "confidence": 0.8952162, "level": "SHOT_LEVEL" }, { "segment": { "startTimeOffset": "9592934", "endTimeOffset": "13596870" }, "confidence": 0.9594428, "level": "SHOT_LEVEL" }, { "segment": { "startTimeOffset": "13638599", "endTimeOffset": "15306991" }, "confidence": 0.9497467, "level": "SHOT_LEVEL" }, { "segment": { "startTimeOffset": "23231533", "endTimeOffset": "26818435" }, "confidence": 0.91483617, "level": "SHOT_LEVEL" }, { "segment": { "startTimeOffset": 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"endTimeOffset": "98056239" } ] } ] } } Using the Cloud Video Intelligence API is a convenient way to extract video content without using your own machine learning systems or computer vision.
Now the Google platform supports the selection of the region where the video material will be processed (to meet regulatory requirements).
Google promises to improve the service by adding new functionality to the Cloud Video Intelligence API during the beta testing period. In fact, now some functions are clearly not enough. For example, the system does not support face recognition. Imagine, from the point of view of the security department, how convenient it would be to simply request the API to determine whether employees A and B met among themselves in the office and next to it over the last year. How often they met and how the average median duration of their conversations increased over a time period.
One can imagine that the object recognition API for video will be used in agriculture, where almost everything is now automated, except for some harvesting and others. With the help of the API, robots will be able to efficiently collect fruits, vegetables and berries. Consequently, the computer system will get rid of the remnants of labor, which now have to attract for harvesting, planting. In the future, such APIs will allow millions of quality inspectors on production lines, warehouse loaders, window cleaners, goods sorters, security guards and other low-skilled workers to be released - advanced machine vision will allow workers to relax in hundreds of different specialties.
With the latest update, Google further expands the Cloud Machine Learning platform, which now includes Vision, Video Intelligence, Speech, Natural Language, Translation and Jobs services. All these interfaces together allow modern companies to use the capabilities of neural networks in their work.
In addition to the Cloud Video Intelligence API, Google made several more announcements in the field of machine vision, including the updated Cloud Vision API to version 1.1 (beta). These are popular interfaces for recognizing objects in images. Since its launch in April 2016, the platform has already processed more than 1 billion images. In the new version, Google added the possibility of advanced character recognition capable of recognizing text on scans of documents with a large amount of text — contracts, books, scientific works.
Recognition of objects in the photo is a handy feature. For example, realtor sites using these APIs provide users with information about real estate through its photos (a person directs a smartphone to a cottage - and gets information about its cost, metric area, number of bedrooms).
Source: https://habr.com/ru/post/373245/
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