{"id":414,"date":"2020-10-16T07:21:07","date_gmt":"2020-10-16T07:21:07","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/10\/16\/detecting-playful-animal-behavior-in-videos-using-amazon-rekognition-custom-labels\/"},"modified":"2020-10-16T07:21:07","modified_gmt":"2020-10-16T07:21:07","slug":"detecting-playful-animal-behavior-in-videos-using-amazon-rekognition-custom-labels","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/10\/16\/detecting-playful-animal-behavior-in-videos-using-amazon-rekognition-custom-labels\/","title":{"rendered":"Detecting playful animal behavior in videos using Amazon Rekognition Custom Labels"},"content":{"rendered":"
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Historically, humans have observed animal behaviors and applied them for different purposes. For example, behavioral observation is important in animal ecology, such as how often the behaviors are, when the behaviors occur, or whether there is individual difference or not. However, identifying and monitoring these behaviors and movements can be hard and can take a long time. To provide an automation for this workflow, a team from the agile members of pharmaceutical customer (Sumitomo Dainippon Pharma Co., Ltd.) and AWS Solutions Architects created a solution with Amazon Rekognition Custom Labels<\/a>. Amazon Rekognition Custom Labels makes it easy to label specific movements in images, and train and build a model that detects these movements.<\/p>\n

In this post, we show you how machine learning (ML) can help automate this workflow in a fun and simple way. We trained a custom model that detects playful behaviors of cats in a video using Amazon Rekognition Custom Labels. We hope to contribute to the afore-mentioned fields, biology and others by publicizing the architecture, our building process, and the source code for this solution.<\/p>\n

About Amazon Rekognition Custom Labels<\/h2>\n

Amazon Rekognition Custom Labels is an automated ML feature that enables you to quickly train your own custom models for detecting business-specific objects and scenes from images\u2014no ML experience required. For example, you can train a custom model to find your company logos in social media posts, identify your products on store shelves, or classify unique machine parts in an assembly line.<\/p>\n

Amazon Rekognition Custom Labels builds off the existing capabilities of Amazon Rekognition<\/a>, which is already trained on tens of millions of images across many categories. Instead of thousands of images, you simply need to upload a small set of training images (typically a few hundred images or less) that are specific to your use case. If your images are already labeled, Amazon Rekognition Custom Labels can begin training in just a few clicks. If not, you can label them directly within the Amazon Rekognition Custom Labels labeling interface, or use Amazon SageMaker Ground Truth<\/a> to label them for you.<\/p>\n

After Amazon Rekognition begins training from your image set, it can produce a custom image analysis model for you in just a few hours. Amazon Rekognition Custom Labels automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics. You can then use your custom model via the Amazon Rekognition Custom Labels API and integrate it into your applications.<\/p>\n

Solution overview<\/h2>\n

The following diagram shows the architecture of the solution. When you have model in place, the whole process of detecting specific behaviors in a video is automated; all you need to do is upload a video file (.mp4).<\/p>\n

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The workflow contains the following steps:<\/p>\n

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  1. You upload a video file (.mp4) to Amazon Simple Storage Service<\/a> (Amazon S3), which invokes AWS Lambda<\/a>, which in turn calls an Amazon Rekognition Custom Labels inference endpoint and Amazon Simple Queue Service<\/a> (Amazon SQS). It takes about 10 minutes to launch the inference endpoint, so we use a deferred run of Amazon SQS.<\/li>\n
  2. Amazon SQS invokes a Lambda function to do a status check of the inference endpoint, and launches Amazon Elastic Compute Cloud<\/a> (Amazon EC2) if the status is Running<\/code>.<\/li>\n
  3. \nAmazon CloudWatch Events<\/a> detects the Running<\/code> status of Amazon EC2 and invokes a Lambda function, which runs a script on Amazon EC2 using the AWS Systems Manager<\/a> Run<\/li>\n
  4. On Amazon EC2, the script calls the inference endpoint of Amazon Rekognition Custom Labels to detect specific behaviors in the video uploaded to Amazon S3 and writes the inferred results to the video on Amazon S3.<\/li>\n
  5. When the inferred result file is uploaded to Amazon S3, a Lambda function launches to stop Amazon EC2 and the Amazon Rekognition Custom Labels inference endpoint.<\/li>\n<\/ol>\n

    Prerequisites<\/h2>\n

    For this walkthrough, you should have the following prerequisites:<\/p>\n