Strategies to Expand Video Surveillance Capabilities

Applied Tech Review | Friday, December 30, 2022

Developers can expand video surveillance functionality by leveraging open-source libraries, vendor add-ons, and ML algorithms.

FREMONT, CA: Analyzing digital traffic captured by video surveillance or live expands surveillance capabilities through algorithms. Developers from specific industries integrate facial recognition, motion detection, object detection, and specific algorithms in the transportation, police, and firefighting sectors.

It is possible to implement algorithms centrally, in the cloud, or at the endpoint device, such as a video surveillance camera. The physical security field often uses algorithms to detect and process images, and companies can use the right algorithms in a wide range of image detection for processing.

Available open-source libraries and modules: IoT-based specialized camera systems, isolated USB cameras, and commercially available camera systems can all be supported by libraries and modules, such as commercially available camera systems that use standard communication methodologies.

Open-source libraries also allow for community-driven updates, which provide wider support. With some open-source platforms offering additional options for dedicated support and licensing through paid services, open-source libraries and modules enable active developers to assist with updates, bug fixes, and support.

Vendor add-on options for video surveillance: Several components can utilize in enterprise-level video surveillance solutions, including infrastructure servers, communication, and network technologies, and endpoints that third parties can integrate. Integrated video analytics with video management systems (VMS) can augment the installed solutions with artificial intelligence (AI).

Vendors can provide large-scale storage options for organizations with large datasets to help meet ongoing retention requirements. It may be necessary to store terabytes of data across multiple geographies in a multi-node surveillance system. Implementing SaaS and APIs allows organizations to manage and access video data more effectively, efficiently, and with greater scalability.

Machine learning, artificial intelligence, and algorithms: Computer vision and digital image processing have been simplified and automated thanks to machine learning (ML). Machine learning algorithms can detect objects within live-streaming videos due to improvements in data streaming, cloud services, and processing technologies.

Machine learning can be used to process digital images, detect feature points, interact with humans, recognize facial patterns, analyze digital documents, and detect signature patterns in banking. Companies apply a multitude of algorithms and libraries for these applications. Other algorithms that distinguish moving items can perform well in low illumination conditions.

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