Hanwha Vision Reveals 2025 Video Surveillance Trends

Generative Artificial Intelligence is set to transform interactions with video systems, predicts Hanwha Vision.

Hanwha Vision has announced its forecast for key trends in video surveillance for 2025, highlighting a transition towards more intelligent systems. This evolution is marked by the development of super-intelligent video surveillance that transcends simple detection to autonomous decision-making based on comprehensive analyses.

One significant trend is the advancement of edge Artificial Intelligence (AI). By enabling powerful AI capabilities within edge devices, such as cameras, Hanwha Vision aims to provide faster decision-making and operational efficiency. Future edge AI cameras are anticipated to function as intelligent agents, capable of understanding and responding to situations autonomously.

Generative AI is another pivotal trend, with potential applications in intrusion detection and fire prevention highlighted. The technology promises to enhance traditional systems by understanding human behavior and context. Additionally, the expansion of AI ecosystems is expected to foster collaboration and innovation in the market. Simplified management solutions, end-to-end integrations, and a focus on cybersecurity and transparency are also predicted to enhance the security landscape in the coming years.

68

Impact Score

Flexible data centers could ease grid bottlenecks

Startups, utilities and chipmakers are testing ways for computing facilities to reduce electricity use during grid stress. The approach could speed connections, but critics warn it cannot replace new generation and transmission.

AMD and Rackspace plan dedicated AI compute rollout

AMD and Rackspace have finalized a phased deployment for dedicated AMD-based compute across Rackspace data centers. The capacity is aimed at regulated enterprise workloads, including clinical AI and large-scale inference.

Lexar tests SSD offloading for local AI models

Lexar is developing an AI-focused SSD approach designed to cut DRAM demand when running large language models on consumer PCs. Internal tests show the company’s storage offloading can load models that traditional local frameworks struggle to run with limited memory.

Contact Us

Got questions? Use the form to contact us.

Contact Form

Clicking next sends a verification code to your email. After verifying, you can enter your message.