Omdia forecasts that by 2025, the number of cameras with built-in deep learning-based analytics will reach 64% of all cameras sold worldwide. According to Moore's Law, the computational capabilities of these devices will grow along with their energy efficiency. 

This will offer the following benefits:

Low Latency and Higher Frame Rates 

Real-time AI-based analysis tasks can be performed without the need to limit frame rates to 10, 15, or 20 frames per second if the application requires higher values. This is crucial for critical applications, such as detecting and tracking fast-moving objects and for adaptive automated systems and interfaces.

Higher Accuracy and Reliability 

Higher video resolution requires greater computational power. High-resolution cameras (FHD, UHD) used for precise detection of small details demand more TOPS (trillion operations per second). Increased computational power allows applications to utilize larger, more accurate modern neural network models. These models are becoming increasingly complex, enhancing result accuracy. Early generations of edge processors and AI accelerators (let alone GPU-based edge products) were more limited in their ability to use such models without significant accuracy reductions.

High-resolution cameras combined with high-precision AI improve the quality of detection and classification, leading to more reliable alerts for users and operators.

More Reliable Capabilities and Richer Applications 

Greater computational power enables the detection of more objects and better identification, along with the simultaneous use of multiple neural network models. Users are not limited to basic applications like line crossing detection, motion detection for objects moving up to 50 km/h, or simple object detection identifying up to 10 types.

These capabilities allow for the concurrent execution of all these operations. Today, most valuable video analytics applications use multiple models to generate more meaningful data.

Cost Savings at the System Level 

More powerful analytics on the camera enable coverage of larger areas of interest. With enhanced video stream processing capabilities, users can deploy higher-resolution or multi-sensor cameras instead of several fixed low-resolution cameras to cover the same area. Enhanced analytic accuracy reduces the volume of stored video and network bandwidth load by creating metadata directly on the device.

Moreover, users can achieve a better combination of the above-mentioned benefits. There's always a trade-off between resolution, frame rate, model size (or the complexity of the analytic application), and solution cost. Greater resource availability reduces the compromises users must make. For example, clients can analyze significant data from multiple video streams simultaneously at high resolution and in real-time frame rates.

By integrating advanced deep learning-based analytics directly into cameras, users can expect numerous benefits, including improved performance, efficiency, and cost-effectiveness. This technological advancement enhances the overall value and functionality of surveillance systems, making them more robust and capable of meeting diverse needs.

TRASSIR helps businesses improve their daily processes by analyzing various features and offering solutions and  cameras with built-in deep learning-based analytics for more efficient production.

Please contact us to order a free remote demonstration of TRASSIR products: 

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