How to Reduce Latency in Real-Time Embedded Video Monitoring Systems?

Real-time embedded video monitoring systems are at the heart of security, automation, and AI-driven applications. But what happens when latency disrupts real-time performance? Delays of even a few milliseconds can mean missed security threats, poor industrial automation response times, or laggy user experiences.

So, how do we fix it? In this guide, we’ll break down the causes of latency in embedded systems and walk through practical solutions to optimize hardware, software, and network performance.

Understanding Latency in Embedded Video Monitoring Systems

Latency is the time delay between capturing a video frame and displaying it on a screen or transmitting it over a network. In real-time embedded systems, lower latency means better responsiveness. High latency, on the other hand, leads to delayed video feeds, missed events in security applications, and sluggish human-machine interactions.

For example, in surveillance systems, a high-latency video feed can mean the difference between preventing an incident and reacting too late. In industrial automation, latency can cause machines to misinterpret real-time sensor data, leading to production errors.

Types of Latency in Embedded Video Systems

Latency can come from multiple sources, and it’s essential to understand them before trying to optimize:

1.Processing Latency – The time taken for the embedded system to process a video frame before sending it to the display or network. This depends on CPU/GPU/NPU performance, video encoding/decoding, and system load.

2.Transmission Latency – The delay introduced when sending video data over wired or wireless networks. Poorly optimized Wi-Fi, Ethernet, or 5G connections can significantly increase lag.

3.Display Latency – The time it takes for the processed video frame to be rendered on a screen. This includes refresh rate limitations, frame buffering, and the efficiency of the display pipeline.

How Hardware and Software Impact Video Latency

Both hardware and software play a crucial role in minimizing latency in embedded video monitoring systems.

Hardware Factors:

Choosing the right SoM/SBC with hardware-accelerated video processing (e.g., VPUs, GPUs, or AI accelerators).
High-speed memory (DDR4/DDR5) and low-latency storage (eMMC, NVMe).
Optimized video encoders/decoders like H.264, H.265, and VP9.

Software Factors:

Using a real-time operating system (RTOS) or low-latency Linux kernel (e.g., PREEMPT-RT).
Fine-tuning driver settings for frame rendering and buffer management.
Optimizing video streaming protocols (RTSP, WebRTC) for minimal transmission delay.

Common Causes of High Latency in Embedded Systems

Before jumping into solutions, let’s look at what might be causing high latency:

1.Inefficient Video Processing Pipelines – If your embedded system doesn’t leverage hardware acceleration properly, the CPU might be overloaded, leading to frame drops and lag.

2. Poor Network Optimization – Using low-bandwidth connections, excessive video compression, or high-latency protocols can slow down transmission speeds.

3.Overloaded System Resources – Running too many background processes, inefficient software drivers, or outdated firmware can impact real-time performance.

4.Suboptimal Display Pipeline – If the system relies too much on frame buffering or a slow refresh rate, it increases the time taken for frames to reach the display.

Now, let’s explore how to fix these problems.

How to Reduce Display Latency in Embedded Video Systems?

1. Optimize Frame Buffering and Refresh Rate

The way frames are handled before they’re displayed can make a huge difference in latency.

•Reduce frame buffering where possible—many systems buffer extra frames to smooth playback, but this increases latency.

•Use displays with a higher refresh rate (e.g., 60Hz or 120Hz) to speed up response times.

•Enable double or triple buffering only when necessary to prevent tearing.

2. Implement Direct-to-Display Rendering

Instead of sending frames through multiple processing layers, direct-to-display rendering allows video frames to bypass unnecessary processing, reducing latency.

•Use hardware overlays instead of software-based rendering.

•Optimize GPU drivers to support direct memory access (DMA) for video output.

•Minimize frame conversion (e.g., YUV-to-RGB) whenever possible.

3. Reduce Input Lag in Touch-Based Embedded Systems

For interactive embedded applications (like industrial HMI touch panels), reducing latency means faster response times.

•Enable low-latency input drivers in Linux or Android-based embedded systems.

•Use touch controllers with high polling rates (e.g., 1000Hz).

•Reduce touch processing overhead in the software stack.

Case Study: Real-Time Video Monitoring with an Embedded System

To illustrate these optimizations in action, let’s look at an example where an embedded video monitoring system was optimized for security surveillance.

Problem:

A security company deployed an AI-powered surveillance system but faced high latency (~250ms), causing delayed threat detection.

Steps Taken to Reduce Latency:

1.Upgraded hardware – Switched to an SoM with a dedicated VPU (Video Processing Unit) instead of relying on CPU-based encoding.

2.Software tuning – Used an RT-PREEMPT Linux kernel for low-latency scheduling.

3.Network optimization – Switched from Wi-Fi to Ethernet and optimized RTSP streaming settings.

4.Reduced buffering – Adjusted GStreamer pipeline to minimize frame delays.

5.Enabled Direct-to-Display – Reduced frame processing steps by optimizing display output drivers.

Results:

After these changes, the system achieved a 70% reduction in latency, cutting response times to under 80ms—fast enough for real-time detection and alerts.

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Final Thoughts: Optimizing Embedded Systems for Real-Time Video Processing

Reducing latency in real-time embedded video monitoring systems requires a combination of hardware acceleration, software tuning, and network optimization. Whether you’re designing an industrial video monitoring system or an AI-driven security camera, every millisecond counts.

By optimizing frame processing, network transmission, and display output, engineers and developers can significantly improve real-time responsiveness and enhance user experience.

Key Takeaways:

✅ Use hardware-accelerated video processing (VPU, GPU, AI accelerators).

✅ Optimize Linux drivers and real-time kernels for low-latency performance.

✅ Minimize frame buffering and display pipeline delays.

✅ Choose low-latency streaming protocols and optimize network connections.

The faster your embedded video system processes frames, the better its real-time performance!