{"id":354086,"date":"2017-01-17T12:36:10","date_gmt":"2017-01-17T20:36:10","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=354086"},"modified":"2021-12-07T14:38:28","modified_gmt":"2021-12-07T22:38:28","slug":"optasia-relational-platform-efficient-large-scale-video-analytics","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/optasia-relational-platform-efficient-large-scale-video-analytics\/","title":{"rendered":"Optasia: A Relational Platform for Efficient Large-Scale Video Analytics"},"content":{"rendered":"

Camera deployments are ubiquitous, but existing methods to analyze video feeds do not scale and are error-prone. We describe Optasia, a dataflow system that employs relational query optimization to efficiently process queries on video feeds from many cameras. Key gains of Optasia result from modularizing vision pipelines in such a manner that relational query optimization can be applied. Specifically, Optasia can (i) de-duplicate the work of common modules, (ii) auto-parallelize the query plans based on the video input size, number of cameras and operation complexity, (iii) offers chunk-level parallelism that allows multiple tasks to process the feed of a single camera. Evaluation on traffic videos from a large city on complex vision queries shows high accuracy with many fold improvements in query completion time and resource usage relative to existing systems.<\/p>\n

Click here for some detailed examples and a demo (opens in new tab)<\/span><\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Camera deployments are ubiquitous, but existing methods to analyze video feeds do not scale and are error-prone. We describe Optasia, a dataflow system that employs relational query optimization to efficiently process queries on video feeds from many cameras. Key gains of Optasia result from modularizing vision pipelines in such a manner that relational query optimization 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