REAL-TIME FACE TRACKING Using Raspberry Pi 4 Versus Raspberry Pi 5 With Hailo-8L

Real-time face tracking has evolved from a research novelty into a practical tool for interactive robotics, surveillance, and automation.

- SOMNATH BERA

Advances in single-board computers and AI accelerators have enabled compact systems to detect and track human faces with remarkable speed and accuracy. This face-tracking system is built on the Raspberry Pi 5, with optional Hailo-8L acceleration, and compares its performance against Raspberry Pi 4, demonstrating how hardware choices affect tracking responsiveness and precision.

Face tracking in this system is not designed to identify or recognise a specific individual. Its purpose is to detect human faces within the camera’s field of view and dynamically track their movement. The objective of the system is to move the pan-tilt-mounted camera to locate human faces, lock onto the region with the highest face density, and count the detected faces. If no faces are present, the system crisscrosses the visual frame to locate them, producing an interactive, almost lifelike behaviour reminiscent of R2-D2 in The Empire Strikes Back movie.

Equipped with a quad-core CPU and up to 8GB or 16GB RAM, it can handle computer vision workloads using OpenCV, TensorFlow Lite, or lightweight YOLO models. For applications that demand higher speed and reliability, a Hailo-8L AI accelerator can be added, providing an additional 8 or 16 TOPS of AI performance. In comparison, microcontroller platforms such as Arduino, ESP32, or MaixCam cannot achieve this level of performance.

Fig. 1 demonstrates real-time face detection in a crowded scene, where multiple human faces are simultaneously identified and highlighted with bounding boxes. Fig. 2 shows the author's prototype. The components required to build the system are listed in the Bill of Materials table.

Cooling requirements