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Real-time video analytics can be used for a wide range of use cases across industries:
- Retail: monitor foot traffic within a store to optimize product placement, enhance loss prevention measures, or display personalized digital promotions
- Financial services: boost security surveillance in bank branches, or display personalized digital promotions at ATMs
- Manufacturing: detect product defects on the production line before they affect an entire batch, or identify when factory workers are missing safety gear
This project demonstrates a deployment of the RedisEdge stack that provides real-time analytics of video streams. It uses Redis Streams, RedisGears, RedisAI, and RedisTimeSeries to count the number of people in a video.
When synchronizing databases, ensure data persistence and availability of captured data changes while mitigating the performance penalty associated with shipping them to the target database.
This example shows how to use the Write Behind pattern to map data from Redis Hashes to MySQL tables. The recipe maps all Redis Hashes with the prefix person:
This demo for RedisGears uses fbprophet for timeseries prediction in anomaly detection.
