Analysis of video content
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; 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.
1. A computer-implemented method comprising:
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;
placing the request into a queue;
polling the queue to retrieve the request;
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.
2. The computer-implemented method of claim 1 , wherein first API request is one of a start content moderation request to perform an analysis of content of the stored video, a start face detection request to perform face detection in the stored video, a start label detection request to perform label detection in the stored video, a start person tracking request to perform person tracking in the stored video, and a start celebrity recognition request to perform celebrity detection in the stored video.
3. The computer-implemented method of claim 1 , wherein the chunk processor includes a chunk decoder to generate chunk frames that are passed to at least one machine learning algorithm to perform the at least one analysis.
4. A computer-implemented method comprising:
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;
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.
5. The computer-implemented method of claim 4 , wherein first API request is one of a start content moderation request to perform an analysis of content of the stored video, a start face detection request to perform face detection in the stored video, a start label detection request to perform label detection in the stored video, a start person tracking request to perform person tracking in the stored video, and a start celebrity recognition request to perform celebrity detection in the stored video.
6. The computer-implemented method of claim 4 , wherein the chunk processor includes a chunk decoder to generate chunk frames that are passed to at least one machine learning algorithm to perform the at least one analysis.
7. The computer-implemented method of claim 6 , wherein the least one machine learning algorithm of the chunk processor is a face detection algorithm.
8. The computer-implemented method of claim 6 , wherein the least one machine learning algorithm of the chunk processor is a label detection algorithm.
9. The computer-implemented method of claim 4 , wherein the joining of the results of the processing of each chunk to generate a final result is performed by an aggregator and the final result includes at least one of a per frame person bounding box, face bounding box, and a face match.
10. The computer-implemented method of claim 4 , wherein the first API request is received by a front end of a video analysis service.
11. The computer-implemented method of claim 4 , wherein the stored video is generated by capturing and indexing streaming video.
12. The computer-implemented method of claim 4 , wherein the stored video is encrypted and is protected from unauthorized access.
13. The computer-implemented method of claim 4 , further comprising:
notifying a requestor that the final result is available.
14. A system comprising:
an end user device to send a first request for analysis of stored video;
a web services provider to:
receive the request to analyze a stored video, the first request to include a location of the stored video and at least one analysis action to perform on the stored video;
access the location of the stored video to retrieve the stored video;
segment the accessed video into chunks;
process 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;
join the results of the processing of each chunk to generate a final result;
store the final result; and
provide the final result to a requestor in response to a second API request.
15. The system of claim 14 , wherein first request is one of a start content moderation request to perform an analysis of content of the stored video, a start face detection request to perform face detection in the stored video, a start label detection request to perform label detection in the stored video, a start person tracking request to perform person tracking in the stored video, and a start celebrity recognition request to perform celebrity detection in the stored video.
16. The system of claim 14 , wherein the chunk processor is to include a chunk decoder to generate chunk frames that are passed to at least one machine learning algorithm to perform the at least one analysis.
17. The system of claim 16 , wherein the least one machine learning algorithm of the chunk processor is a face detection algorithm.
18. The system of claim 16 , wherein the least one machine learning algorithm of the chunk processor is a label detection algorithm.
19. The system of claim 14 , wherein the joining of the results of the processing of each chunk to generate a final result is performed by an aggregator and the final result includes at least one of a per frame person bounding box, face bounding box, and a face match.
20. The system of claim 14 , wherein the first request is received by a front end of the web services provider.