Surveillance systems are increasingly relying on embedded vision technologies to speed up deployment across a wide range of markets and applications. These systems are used in various fields such as traffic monitoring, security, intelligence, surveillance, and reconnaissance (ISR), and business analytics. The diversity of these applications brings several challenges that system designers must address effectively. Key challenges include:
- **Multi-camera vision** – The ability to connect multiple homogeneous or heterogeneous sensor types.
- **Computer vision technology** – The need for advanced libraries and frameworks like OpenCV and OpenVX.
- **Machine learning technology** – The capability to implement machine learning inference engines using frameworks like Caffe.
- **Higher resolution and frame rates** – Increased data processing demands per image frame.
These systems often use algorithms like optical flow for motion detection, while stereo vision provides depth perception. Machine learning techniques are also employed for object detection and classification within images.
Heterogeneous devices, such as the All Programmable Zynq®-7000 and Zynq® Ultrascale+™ MPSoC, are becoming essential in developing modern surveillance systems. These devices combine programmable logic (PL) with high-performance ARM® cores, offering a powerful platform for real-time image processing.
The tight integration of PL and PS allows for more responsive, reconfigurable, and energy-efficient systems compared to traditional CPU/GPU-based solutions. In conventional setups, image data must be transferred through shared memory, causing delays and bottlenecks. This becomes even more problematic with higher resolutions and frame rates.
By implementing image processing pipelines in the PL of Zynq devices, developers can create true parallel processing pipelines, ensuring deterministic response times and lower latency. Additionally, the flexible I/O interfaces of PL support industry standards like MIPI, Camera Link, and HDMI, allowing for multiple camera connections and future upgrades.
However, the challenge lies in implementing complex algorithms without rewriting them in hardware description languages like Verilog or VHDL. This is where the **reVISIONâ„¢ stack** comes into play.
The reVISION stack supports developers in implementing computer vision and machine learning techniques on Zynq-7000 and Zynq UltraScale+ MPSoC devices. It consists of three layers:
1. **Platform Development** – Provides the foundation for building the rest of the stack using SDSoC tools.
2. **Algorithm Development** – Enables the implementation of image processing and machine learning algorithms in the PL.
3. **Application Development** – Offers industry-standard framework support, allowing developers to build end-to-end applications.
At the algorithm layer, developers can use OpenCV for image processing, accelerating functions like edge detection, filtering, and pixel operations. For machine learning, the stack includes predefined hardware functions for inference engines, which are then accessed by the application layer to integrate with frameworks like OpenVX and Caffe.
One of the key advantages of the reVISION stack is its ability to accelerate OpenCV functions, divided into four categories: calculations, input processing, filtering, and other features like edge detection and classifiers. These are tightly integrated with OpenVX, enabling efficient pipeline creation in the PL.
In terms of machine learning, reVISION integrates with **Caffe**, allowing developers to implement inference engines by simply providing a prototxt file. The framework handles the rest, including configuring the processing system and optimizing the hardware. This makes it easier to deploy models and reuse pre-trained networks from the Caffe model zoo.
Moreover, reVISION supports fixed-point representations like INT8, which improve performance and reduce power consumption. Fixed-point operations are simpler to implement in PL and allow for efficient use of DSP blocks, making them ideal for real-time applications.
In conclusion, the reVISION stack empowers developers to leverage the full potential of Zynq devices, enabling faster development and more efficient systems. Whether you're an expert or a beginner, this stack simplifies the process of implementing complex algorithms and machine learning models, resulting in systems that are more responsive, reconfigurable, and power-efficient.
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