Multi-Camera Configurations and Synchronization
- ridgerun
- 3 days ago
- 5 min read
In previous articles, we discussed camera interfaces and camera shutter technologies. Now, we'll explore how embedded vision applications increasingly use multiple cameras (for stereo depth, 360° vision, or just increased coverage). Key challenges in multi-camera setups include synchronization of frames, handling increased data bandwidth, and managing per-camera controls and metadata.
Table of Contentes
Synchronization
To capture frames at the same time from multiple camera interfaces, there are hardware and software approaches:
Hardware sync: Many camera sensors support a sync input or master-slave configuration. One sensor (master) drives a synchronization signal (like frame start pulse) to others (slaves). This ensures all sensors expose simultaneously. With rolling shutter sensors, synchronization usually aligns the start of readout, but due to the rolling nature, there could still be microsecond differences. With global shutter, a sync pulse can truly line up exposures.
Shared clock: Another hardware method is to use the same clock source for all sensors (e.g., feed the same reference clock or oscillator output to each camera), so their frame timing is inherently aligned. This often goes along with a sync signal for start-of-frame.
SerDes sync: In SerDes systems (FPD-Link III / GMSL), the deserializer often can generate a synchronized frame sync output to all connected cameras. For example, TI’s FPD-Link III chipset can distribute a common synchronization signal to ensure all cameras on a deserializer start frame capture together.
Software sync: If hardware sync isn’t possible, software methods like timestamping frames and aligning them in software (dropping or delaying frames to align in time) are used. This is less precise (usually only millisecond precision via system timestamps) and can add latency. It’s more of a last resort if cameras cannot be hardware-synced.
Metadata and Triggering
Some camera interfaces output a frame counter or timestamp in embedded metadata (special data packets in the CSI-2 stream), which can help in verifying sync or doing software alignment. Drivers need to be able to capture and expose this metadata. On Jetson, the Argus camera stack can ingest embedded metadata lines if the driver marks them properly in the device tree (e.g., designating certain CSI packet types as stats or embedded data). On i.MX8, the V4L2 API would deliver metadata either via separate V4L2 buffers or sideband (this is still an evolving area – not all platforms support metadata out-of-the-box).
Bandwidth
Using multiple cameras naturally increases total throughput. A Jetson AGX Xavier, for instance, can handle multiple 4K cameras by using multiple CSI interfaces or by using virtual channels over a few CSI interfaces (with aggregated deserializers). The camera driver developer must configure the vi/ISP capture settings accordingly to allocate resources for each stream. NVIDIA provides guidelines on the maximum cameras (e.g., an Orin might support up to 16 cameras via 16 virtual channels on multiple CSI ports). On NXP i.MX8, there might be limitations like total pixels per second that the ISI or DDR can handle.
Driver and User-space
RidgeRun’s camera drivers often include support for multi-camera by either instantiating multiple V4L2 devices (one per camera) or one device with multiple video nodes. For synchronized capture, user-space can use the VIDIOC_SYNC mechanisms or simply ensure that it triggers all cameras and waits for frames. In GStreamer, one might use the ts-offset or synchronization properties to align streams, or custom pipeline elements that merge or sync frames (RidgeRun has built, for example, an image stitcher that merges multiple camera feeds in real-time for surround view, and that requires tight sync between inputs).
Example: Signal Flow from Sensor to Application
To tie everything together, let’s consider a typical signal flow for a camera interface on an embedded platform (say, a GMSL camera on a NVIDIA Jetson):
Image Sensor – captures images (rolling or global shutter) and outputs raw data (often Bayer RAW10/12) over MIPI CSI-2.
Serializer/Bridge – if used (like GMSL serializer or an HDMI-to-CSI bridge), it takes the sensor output and encodes it for transmission (coax cable or other medium).
Physical Link – e.g., a 15m coax cable carries the high-speed serial data. For MIPI CSI (no SerDes), this step is just a short FPC cable or board trace.
Deserializer/Receiver – converts data back to MIPI CSI-2 (for SerDes), or directly feeds the SoC if it’s already CSI-2. On Jetson, this is where the data hits the Tegra CSI-2 receiver block.
SoC CSI-2 Receiver – hardware IP that parses CSI-2 packets, separates video streams (by virtual channel ID if needed), and forwards them to the memory or ISP. On Jetson, this is the “VI” (video input) and on i.MX8 it’s the ISI/CSI capture interface.
Image Signal Processor (ISP) – Many SoCs have an ISP to convert raw Bayer data to usable RGB/YUV (debayering, noise reduction, etc.). Jetsons have a built-in ISP that can be used via the libargus camera stack or the nvarguscamerasrc in GStreamer. If using raw data directly (or if the sensor has its own ISP or is YUV output), this stage might be bypassed.
Kernel Drivers—The V4L2 driver (developed by teams like RidgeRun) orchestrates the above, configuring the sensor (via I²C), setting up the SerDes chip (also via I²C to route the links), and programming the SoC capture pipeline (via media controller and video node setup). Once streaming, it mediates buffer exchange, delivering frames to user space.
User-Space Application – This could be a GStreamer pipeline (e.g., using v4l2src for raw frames or nvarguscamerasrc for Jetson to get ISP processed frames), OpenCV grabbing from /dev/videoX, or a custom app using V4L2 ioctls or NVIDIA libargus. The application receives frames and can then display, encode, or run computer vision algorithms (like TensorRT for AI inference).
Synchronization & Control – If multiple cameras are in use, an additional synchronization mechanism might be employed either in driver (e.g., trigger all sensors together) or in user-space (align frames by timestamp). Control software might also adjust camera settings (exposure, gain) via V4L2 controls, potentially coordinating between cameras for consistent imaging.
Throughout this chain, careful attention is needed to maintain signal integrity (especially for high-speed MIPI or SerDes links), proper driver timing (so frames aren’t dropped), and metadata (e.g., tagging frames with timestamps or sequence numbers). RidgeRun’s extensive experience in camera driver development (over a decade of projects on V4L2 drivers) means these considerations are well understood – from device tree configurations on Jetson to dealing with ISP calibration files and tuning or extending support for custom controls (auto-white-balance, HDR, etc.).
Conclusion
In embedded vision development, choosing the right camera interface and camera shutter technology is as important as selecting the processor. With platforms like NVIDIA Jetson AGX Xavier/Orin and NXP i.MX8, developers have a solid foundation of CSI-2 inputs and ISP capabilities to build complex vision systems. The key is software support: device drivers and system integration.
This is where RidgeRun’s expertise comes in—with years of experience building V4L2 drivers for sensors and SerDes (including GMSL, FPD-Link III/IV, HDMI bridges, and more), handling multi-camera synchronization, and delivering seamless user-space integration using GStreamer and OpenCV. RidgeRun has enabled customers to capture from 6+ cameras in sync on Jetson, deploy custom thermal solutions, and optimize performance across the entire stack.
By understanding the strengths of each interface and shutter type—and leveraging proven development practices—you can confidently design an embedded vision system that meets your needs for bandwidth, range, and image quality.
For more technical depth or assistance with custom camera drivers, consult RidgeRun’s Developer Wiki or reach out to our team—we’re here to help turn cutting-edge camera technology into reality on embedded Linux platforms.
