CODEWITHSUNDEEP
amazon-web-servicesobject-detectionamazon-sagemaker
CodingAddict
CodingAddict

Reputation: 223

How to perform Real Time Object Detection with trained AWS model

After successfully training object detection model with AWS SageMaker, how do I use this model to perform real time object detection on RTSP video?

Upvotes: 2

Views: 361

Answers (2)

Teodorico Levoff
Teodorico Levoff

Reputation: 1659

One solution for real-time object detection is as follows.

After training your model, upload your model to S3. You can check this using

$aws sagemaker list-training-jobs --region us-east-1

Then you need to attach your trained model to a Amazon SageMaker endpoint. You can do this using this:

object_detector = estimator.deploy(initial_instance_count = 1,                                 
instance_type = 'ml.g4dn.xlarge')

After your model is attached to an endpoint you will need to create an API that will allow users to pass input to your trained model for inference. You can do this using Serverless. With Serverless, you can create a template which will create a Lambda function as handler.py and serverless.yml which needs to be configured on how your application will operate. Make sure that in serverless.yml you specify your endpoint name, SAGEMAKER_ENDPOINT_NAMEas well as Resource: ${ssm:sagemakerarn}. This is an allow policy resource (AWS Systems Manager Agent) parameter that needs to be passed in. In your lambda function, make sure you invoke your SageMaker endpoint.

From here you can now deploy your API for real-time detection:

serverless deploy -v

Finally, you can use curl to invoke your API.

See here for a detailed walk-through.

Upvotes: 1

j2abro
j2abro

Reputation: 753

You haven't specified where you will host the model: on AWS, a mobile device or something else. However, the general approach is that your model, assuming a CNN that processes images, will consume one frame at a time. You haven't specified a programming language or libraries so here is the general process in psuedocode:

while True:
    video_frame = get_next_rtsp_frame()
    detections = model.predict(video_frame)
    # There might be multiple objects detected, handle each one:
    for detected_object in detections:
        (x1, y1, x2, y2, score) = detected_object # bounding box
        # use the bounding box information, say to draw a box on the image

One challenge with a real-time video stream requirement is avoiding latency, depending on your platform and what kind of processing you do in your loop. You can skip frames, or don't buffer missed frames, to address this.

Upvotes: 0

Related Questions