Amazon Technologies, Inc.

Shubham Chandra Gupta

Vivek Bhadauria

Joseph Patrick Tighe

Satishkumar Kothandapani Shanmugasundaram

Jonathan Hedley

Alessandro Bergamo

Gowtham Jeyabalan

Prathyusha Reddy Cheruku

Arpita Agrawal

Andrea Olgiati

Vasant Manohar

Rakesh Madhavan Nambiar

Nitin Singhal

Jiazhi Ou

Praveenkumar Udayakumar

Palak Mehta

Mark Hawley Yang

Techniques for unique label recognition and notification for streaming video. A video stream from a camera-equipped electronic device is received via the API endpoint of a stream processing service in a provider network. A request for unique label recognition and notification is received via an API endpoint of a computer vision service in the provider network. The computer vision service analyzes a video fragment of the video stream. If a unique label is recognized in the video fragment, then a notification service in the provider network sends a notification that the unique label was recognized in video captured by the camera-equipped electronic device.

Unique label recognition and notification for streaming video

Techniques for label recognition and notification for streaming video from non-overlapping cameras. A stream processing service of a provider network receives a first video stream from a first camera-equipped electronic device via an API endpoint of the stream processing service. The stream processing service also receives a second video stream from a second camera-equipped electronic device an API endpoint of the stream processing service. Meanwhile, a request for label recognition and notification is received at a computer vision service of the provider network via an API endpoint of the computer vision service. In response, the computer vision service recognizes a label in a video fragment of the first camera video stream and recognizes a label in a video fragment of the second camera video stream, and then identifies whether the two labels are the same label. If so, a notification service of the provider network sends a notification indicating that the label was recognized across non-overlapping cameras.

Label recognition and notification for streaming video from non-overlapping cameras

Techniques for analyzing stored video upon a request are described. For example, a method of receiving a first application programming interface (API) request to analyze a stored video, the API request to include a location of the stored video and at least one analysis action to perform on the stored video; scheduling a job for the first API request using a global scheduler, the global scheduler to schedule, based at least in part on available bandwidth of processing components including a segmenter, a chunk processor, and a reducer, at least one job queue associated at least one of the processing components; accessing the location of the stored video to retrieve the stored video; segmenting the accessed video into chunks; processing each chunk with a chunk processor to perform the at least one analysis action, each chunk processor to utilize at least one machine learning model in performing the at least one analysis action; joining the results of the processing of each chunk to generate a final result; storing the final result; and providing the final result to a requestor in response to a second API request is described.

Per component schedulers making global scheduling decision

Techniques are described for automatically scaling (or 'auto scaling') compute resources&#x2014;for example, virtual machine (VM) instances, containers, or standalone servers&#x2014;used to support execution of service-oriented software applications and other types of applications that may process heterogeneous workloads. The resource requirements for a software application can be approximated by measuring 'worker pool' utilization of instances of each service, where a worker pool represents a number of requests that the service can process concurrently. A scaling service can thus be configured to scale the compute instances provisioned for a service in proportion to worker pool utilization, that is, compute instances can be added as the fleet's worker pools become more &#x201c;busy,&#x201d; while compute instances can be removed when worker pools become inactive.

Automatically scaling compute resources for heterogeneous workloads

The positioning of elements of a broken word can be corrected by receiving an optical character recognition (OCR) conversion of a printed publication and identifying multiple parts of the broken word from the OCR conversion to output in a graphical user interface (GUI). The multiple parts can be placed in the GUI using original positioning data for the printed publication. A user can make a selection in the GUI indicating that multiple parts from the OCR conversion are of the broken word and can automatically adjust bounds of the multiple parts to form a corrected word.

System for detecting and correcting broken words

The positioning of elements of a broken word can be corrected by receiving an optical character recognition (OCR) conversion of a printed publication and identifying multiple parts of the broken word from the OCR conversion to place in a graphical user interface (GUI). The multiple parts can be placed in the GUI using original positioning data for the printed publication. A user can make a selection in the GUI indicating that multiple parts from the OCR conversion are of the broken word and can automatically adjust bounds of the multiple parts to form a corrected word.

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Seattle, WA, United States of America

Shubham Chandra Gupta