Embedded Computer Vision, End to End: From Sensor Pipeline to Series Product — Key Takeaways from “Embedded Computer Vision” by Clemens Reisner (Mission Embedded)

Why embedded vision matters right now

In the session “Embedded Computer Vision,” Clemens Reisner, Embedded Systems Expert and Team Lead of the Computer Vision Team at Mission Embedded, lays out how visual sensing and perception make their way into real-world products. The everyday examples he points to are telling: automated license plate readers in modern car parks, emergency brake assistance systems using a windshield camera, and smartphone cameras that detect faces to optimize image settings. These are not future promises; they are embedded computer vision in action, operating continuously and reliably on tailored hardware.

Mission Embedded’s work spans transportation, railway, industrial, medical, and air traffic management domains. The statement that resonated most with us as an editorial team: “We embed advanced technologies like ears, eyes, voice, and the brain into the products of our customer.” It neatly encapsulates the core technology areas — sensor technology, wireless communication, safety and security, computer vision, artificial intelligence, and deep learning — all in the context of embedded systems.

What defines embedded computer vision

Reisner structures an embedded vision system around five pillars:

• Sensor technology (typically imaging sensors/cameras)
• An embedded system (to acquire and process data)
• Computer vision algorithms (to extract useful image information)
• AI/deep learning (to make algorithms truly “smart”)
• Data (to train, test, and validate)

At the heart of this stack is a processing pipeline that transforms raw pixels into decisions and actions. Vision is computationally intensive, so the hardware must offer sufficient compute headroom — often with accelerators — while meeting constraints in cost, power, form factor, and robustness.

Imaging sensors: perceiving like a human at a crosswalk

Reisner uses a practical analogy: crossing a pedestrian crossing. You identify road markings, interpret a traffic signal, look left and right for vehicles, track objects with your eyes, estimate speed, and make a decision. Imaging sensors, paired with the right algorithms, replicate this chain of perception. Modern cameras are small and inexpensive, so they’re everywhere. The catch: turning raw images into reliable information requires careful engineering — from acquisition and pre-processing to algorithm design and deployment — matched to the sensor, the use case, and the embedded platform.

Embedded systems: tailored compute under constraints

Embedded systems are tailored computers: specific interfaces, operation under harsh conditions and resource constraints, and 24/7 availability at optimized cost. Reisner highlights:

• Custom interfaces to sensors and the environment
• Resource and power constraints by design
• 24/7 operation and robust environmental tolerances
• Cost-optimized hardware
• Enough computational power to run vision workloads

For engineering teams, that means architecting for the target from the start. A proof-of-concept that “just runs” on a laptop needs to become efficient and deterministic on the device — often via C/C++ reimplementation and accelerator use (GPU or neural network accelerators).

Computer vision algorithms: clarity of purpose, pre-processing, optimization

“Algorithms extract useful image information.” That starts with understanding the function and the use case precisely. Pre-processing is critical: de-mosaicing, tone mapping, color correction, and other steps help you get the best out of the raw signal before feature extraction, tracking, or feeding a neural network. Reisner’s emphasis is on professional software development: implement, optimize, and test — for the target hardware.

In practice, a significant portion of engineering effort precedes AI: sensor control, signal conditioning, and classical CV steps stabilize inputs so downstream methods can perform consistently.

AI and deep learning: supervised training offline, real-time inference online

According to Reisner, deep learning has enabled many computer vision applications that were previously out of reach. In this field, “AI” typically means deep learning, most often supervised. Models are trained offline on recorded and annotated datasets until a given accuracy is achieved, then deployed to the embedded system for real-time inference in the customer’s product.

A key implication: convolutional networks can collapse multi-step pipelines (e.g., feature extraction plus tracking) into a single stage. Still, models live within a full system — you need a pipeline that acquires, pre-processes, interprets, and hands off to business logic for action.

Data is the new oil: why collection and annotation are central

Reisner calls out the well-known “data is the new oil” line and translates it into concrete practice. For each use case, Mission Embedded conducts a customized data recording campaign:

• Build a tailored sensor setup for the specific platform (e.g., vehicles, construction machines)
• Account for the real operating environment (outdoor vs. cabin, high/low temperatures)
• Record with target algorithms in mind
• Afterward: annotation (bounding boxes, etc.) and quality checks

This is costly and effort-intensive — and absolutely essential. Without a representative dataset and disciplined annotation/QA, robust training, testing, and validation are not possible. The engineering takeaway: plan your data strategy early, set up collection and annotation professionally, and close the feedback loop between models, labels, and testing.

From idea to series product: the mission embedded project flow

The project lifecycle follows a clear arc:

1. Idea/use case: The customer wants to innovate a product or introduce a new function.
2. Brainstorming with sales engineering: explore options and approaches together.
3. Proof of concept: assemble existing hardware platforms and software building blocks, add Python scripts and glue code, and build an early demo as quickly as possible.
4. Joint evaluation: assess strengths and weaknesses with the customer.
5. Industrialization: develop a tailor-made hardware platform based on nailed-down requirements; port and often reimplement software modules in C/C++ to optimize for the hardware target.
6. Standards and processes: account for security and safety and apply established processes for product quality.
7. Delivery: provide the first series to the customer for integration into their product.
8. Support and evolution: lifecycle management, continuous improvement, and bug fixing.

This flow underlines how iterative validation reduces risk: demonstrate quickly, learn with the customer, then invest in robust hardware/software engineering and process quality.

The pipeline architecture: from sensor to action

Reisner presents a typical architecture as an image processing pipeline composed of hardware and software blocks. The data flow:

• Sensor: provides input (e.g., raw image data)
• Image acquisition: software module to receive/transfer frames
• Pre-processing: extract the best from raw data (de-mosaicing, tone mapping, color correction, etc.)
• Algorithms: a sequence of classical CV stages (e.g., feature extraction, then a higher-level tracker)
• Alternative: a convolutional neural network that performs these tasks in one step
• Business logic: use-case-specific interpretation and decision making
• Action: a hardware interface to communicate with the environment or a superordinate system
• Compute: execution on custom hardware with CPU cores and accelerators (GPU, neural network accelerators)

This modular view is a practical blueprint: clear interfaces, measurable stages, and separation between perception and decision enable testing, replacement of components, and scaling.

The technology stack: languages, frameworks, infrastructure

Reisner offers a comprehensive overview of the tooling and platforms used.

Programming languages

• C and C++ for optimized algorithms on embedded targets
• Python for rapid prototyping
• Shell scripting around the ecosystem
• TypeScript and Java for web interfaces on embedded systems (also JavaScript and common web frameworks, plus databases)
• Requirements as the structured language shared with customers

Operating systems and build environment

• Linux as the default (standard distributions; custom distributions with Yocto when needed)
• Sometimes a real-time operating system is required
• Unix-like build environment: Bash, CMake, Makefiles, unit testing, automated testing

Computer vision and multimedia

• For prototyping: OpenCV, Robot Operating System (ROS), Point Cloud Processing Library (PCL)
• In industrialization: reimplement and optimize specifically for the target platform
• Multimedia/streaming/processing: GStreamer and FFmpeg

Deep learning

• PyTorch, TensorFlow, and ONNX

Development infrastructure

• Microsoft Azure
• Docker registry
• Jenkins build server
• Widely used Git tools

Hardware engineering

• Altium Designer
• Hyperlink Simulation
• SolidWorks
• Support for high-speed designs, rapid prototyping, and proof-of-concept
• Support in the EMC laboratory for pre-qualification and certification

Taken together, this stack spans from the sensor front end to CI/CD infrastructure. Teams aiming to industrialize embedded vision need this breadth — and the orchestration to carry a PoC to a stable series product.

Practical lessons for engineering teams

We distilled the following actionable lessons from the talk:

• Validate early, demo fast: A PoC built from existing platforms and Python scripts quickly reveals feasibility, quality, and weak points — and enables joint evaluation with the customer.
• Treat sensor and pre-processing as first-class: de-mosaicing, tone mapping, and color correction are foundational to stable recognition; sensor know-how must be in the team.
• Make data a first-class workstream: per-use-case data recording campaigns that match environments and target algorithms, followed by annotation and quality checks. The effort is significant — and indispensable.
• Optimize for the target: reimplementation in C/C++ and the use of accelerators are standard to reach real-time performance and efficiency in 24/7 operation.
• Bake in security and safety: standards and processes are core to industrialization and quality.
• Architect as a pipeline: clear modules (acquisition, pre-processing, algorithms, business logic, actuation) improve testability and replaceability, even when a CNN later subsumes multiple stages.
• Own your OS and toolchain: Linux/Yocto, RTOS options where needed, and a Unix-flavored build workflow with CMake/Make, unit and automated tests — backed by Jenkins, Docker, and Git — provide the spine for reproducible builds and continuous integration.

The examples and their implications

The examples in the talk span different operational regimes:

• License plate recognition in car parks: a stationary setup with a camera to read plates and control access.
• Emergency braking assistance: a windshield-mounted camera that monitors the road and triggers automatic braking in case of potential collision.
• Smartphone face detection: detection that adapts camera settings for the best shot.

While they vary in criticality and environment, they share the same fundamental pattern: sensing with cameras, robust real-time processing, and a business logic that translates perception into an action or adjustment.

Business logic: turning perception into action

Reisner repeatedly emphasizes that, by the end of the pipeline, the system must “do something” with derived information. Business logic integrates perception outputs with the specific behavior required by the application, and the hardware interface connects decisions to the outside world or to a superordinate system. This separation keeps the perception stack focused while making actions auditable and testable.

Performance, constraints, and 24/7 operation

Embedded vision doesn’t just need to run; it needs to run reliably and efficiently around the clock. That demands:

• Careful resource management (CPU, memory, power)
• Use of accelerators (GPU, NN accelerators) when beneficial
• Robust thermal and mechanical design at the hardware level
• Testability and system-level quality assurance

Reisner’s point about reimplementing modules in C/C++ for the target underlines that performance work is not an afterthought; it is central to moving from PoC to production.

Working at Mission Embedded: interdisciplinary, growing, hands-on

Reisner closes by describing the work environment at Mission Embedded: more and more products include embedded computer vision, making it a strong field for developers who enjoy working with leading-edge technologies and combining multiple disciplines. The team works in a flexible setup, is growing, and is looking for support — underscoring the momentum in this domain.

Closing thoughts: a clear map for embedded vision engineering

“Embedded Computer Vision” by Clemens Reisner (Mission Embedded) provides a clear, practice-oriented map:

• Take sensor handling and pre-processing seriously.
• Prototype quickly with Python and existing platforms, but align early with a C/C++ optimization path.
• Use deep learning as a powerful building block: train offline, infer in real time on-device.
• Treat data collection, annotation, and QA as core to the project, not side work.
• Architect as a pipeline with strong business logic and well-defined interfaces.
• Bring standards, security/safety, and certification into view early.
• Operate a cohesive stack: OpenCV/ROS/PCL, GStreamer/FFmpeg, PyTorch/TensorFlow/ONNX, and CI infrastructure with Azure, Docker, Jenkins, and Git.

For teams bringing computer vision into embedded products, this combination of discipline and pragmatism is the bridge from an idea to a durable, production-ready system.