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.

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