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The Ultra96-V2 tutorial demonstrates a two-stage Python pipeline: a DenseBox face detector finds faces in each frame, then a centroid tracker associates detections across frames. It uses Vitis-AI 1.1 and the VART runtime, so its setup is specific to an older board image and software stack—not a verified set of current installation instructions. Avnet now lists the Ultra96-V2 as no longer in production.
How the face detection and tracking pipeline works
Detection and tracking are separate jobs. For each frame, the detector returns face bounding boxes. The tracker then compares detections across successive frames and associates them using their centroids. That association can help follow a detected face as it moves, but it is not face recognition: the tutorial does not identify a person or verify identity.
Detection with DenseBox
The tutorial reuses a pre-optimized DenseBox model sized for 640×360 input; it does not train a new face-detection model. Its Python code initializes a VART runner from /usr/share/vitis_ai_library/models/densebox_640_360 and wraps it in the tutorial’s FaceDetect class to process frames.
Association with a centroid tracker
After detection, a simple centroid-based object tracker associates the bounding boxes across frames. This is a lightweight tracking approach, but the tutorial does not compare it with other trackers or report how well it handles missed detections, overlapping faces, or longer occlusions.
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What hardware and software the tutorial uses
The example targets an Ultra96-V2 board running a Vitis-AI 1.1-enabled image. Avnet identifies the board as an AMD/Xilinx Zynq UltraScale+ MPSoC ZU3EG A484 platform with 2 GB of LPDDR4. It boots from microSD and includes Wi-Fi, Bluetooth Low Energy, USB connections, mini-DisplayPort, and low- and high-speed expansion headers. Avnet states that both Ultra96 and Ultra96-V2 are no longer in production, so availability depends on remaining inventory or the used market. Avnet’s Ultra96-V2 product and support page has the board details and lifecycle status.
The tutorial’s listed project equipment includes the Ultra96-V2, a Logitech HD Pro webcam, and a DisplayPort monitor. A USB webcam provides live video input in the example; the tutorial does not establish compatibility with every webcam or newer board image.
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What reproducing the 2020 setup involves
The Hackster.io project, published July 13, 2020 under an Apache-2.0 license, describes a complete board-oriented setup rather than a Python-only install. Its instructions depend on a Vitis-AI 1.1 Ultra96-V2 SD-card image, a 16 GB microSD card, tutorial files, VART runtime configuration, and display configuration. The page references a prebuilt image and archive that were current when it was written; their availability and dependencies should be checked before relying on them today. Read the Hackster.io tutorial.
- Prepare the board image: use the Vitis-AI 1.1-enabled Ultra96-V2 image described by the tutorial and a 16 GB microSD card. Do not assume that its historical download remains available or that the image works with a newer software stack.
- Install the project files and runtime setup: follow the tutorial’s instructions for its files, VART configuration, and display configuration. The example expects the DenseBox model directory at
/usr/share/vitis_ai_library/models/densebox_640_360. - Connect video and display hardware: the project lists a webcam and DisplayPort monitor. Confirm that the camera and image are recognized by the specific board setup before running the scripts.
- Run the selected script variant: the project lists single-threaded and multi-threaded face-detection, face-tracking, and passthrough scripts. The available description does not establish that the multi-threaded versions are faster.
What this example does—and does not—establish
The tutorial names Haar cascades, HOG with SVM, and deep neural networks such as SSD and DenseBox as possible face-detection approaches, but it implements the DenseBox/VART path. Those approaches are not compared experimentally in the source. Choosing another detector means considering its model and runtime compatibility, the target image size and available compute, and the effort of integrating it with the board’s software.
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Neither the tutorial nor the board information establishes detection accuracy, latency, frame rate, power consumption, or comparative performance. Treat the project as an implementation example, not evidence that this pipeline meets a particular real-time or accuracy requirement. The source describes a historical software image; it does not verify a current replacement image or current dependency versions.
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