{"id":164366,"date":"2025-06-03T15:25:25","date_gmt":"2025-06-03T09:55:25","guid":{"rendered":"https:\/\/www.electronicsforu.com\/?p=164366"},"modified":"2025-06-10T19:45:19","modified_gmt":"2025-06-10T14:15:19","slug":"smallest-ai-streaming-camera-diy","status":"publish","type":"post","link":"https:\/\/www.electronicsforu.com\/electronics-projects\/smallest-ai-streaming-camera-diy","title":{"rendered":"A Tiny AI Wireless Streaming Camera"},"content":{"rendered":"
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\"efy<\/figure><\/div>\n\n\n

AI cameras are everywhere\u2014spotting people, detecting objects, and powering advanced driver assistance systems in cars. But most are too bulky and power-hungry for compact applications like drones, wearables, or wildlife tracking.<\/p>\n\n\n\n

This device demonstrates how machine learning can be embedded into a tiny package\u2014perfect for places where size, power, and real-time intelligence matter. <\/p>\n\n\n\n

By using TensorFlow.js with the COCO-SSD model, the camera identifies people, vehicles, and animals directly within a browser, combining intelligent software with minimal hardware. This serves as the foundation for designing one of the smallest AI streaming cameras. <\/p>\n\n\n\n

Fig. 1 shows the author detecting a person and a potted plant during testing.<\/p>\n\n\n

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Fig. 1: Author detecting a person and a potted plant during testing<\/figcaption><\/figure><\/div>\n\n\n
Bill of Materials<\/strong><\/td><\/tr>
Item<\/strong><\/td>Remarks<\/strong><\/td>Quantity<\/strong><\/td><\/tr>
IndusBoard Coin V2<\/a><\/td>Tiny board<\/td>1<\/td><\/tr>
OV2640 camera module<\/td>2MP camera with JPEG output<\/td>1<\/td><\/tr>
Jumper wires<\/td>For hooking it all up<\/td>15<\/td><\/tr>
Power supply<\/td>3.3V battery or USB adaptor<\/td>1<\/td><\/tr>
Micro USB cable<\/td>For programming and power<\/td>1<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Why go small?<\/strong><\/h2>\n\n\n\n

Compact AI cameras offer clear advantages across diverse sectors. <\/p>\n\n\n\n

In drones, they lower payload while supporting obstacle detection and object tracking. In wearables, they integrate discreetly into glasses or badges to deliver real-time visual feedback. In home security, they enable unobtrusive surveillance. For wildlife monitoring, their size allows placement in tight or concealed spots such as birdhouses. In robotics, they serve as visual inputs for navigating constrained environments.<\/p>\n\n\n\n

These use cases require devices that are intelligent, low-power, and exceptionally compact. A development board meeting all three criteria\u2014capable of capturing frames, streaming video, and maintaining a minimal footprint\u2014was essential. <\/p>\n\n\n\n

The IndusBoard Coin V2, just 3.3cm across (approximately coin-sized), was chosen for its compact design and built-in sensors. The Bill of Materials table lists the components used; Fig. 2 shows the assembled prototype.<\/p>\n\n\n

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Fig. 2: Author\u2019s prototype<\/figcaption><\/figure><\/div>\n\n\n

Circuit Diagram<\/strong><\/h2>\n\n\n\n

Fig. 3 illustrates the wiring between the IndusBoard Coin and the OV2640 camera module. Key GPIO pin connections include:<\/p>\n\n\n\n

XCLK (GPIO 44): Supplies a 20MHz clock signal<\/p>\n\n\n\n

PCLK (GPIO 39): Synchronises pixel data<\/p>\n\n\n\n

VSYNC (GPIO 21): Indicates the start of a new frame<\/p>\n\n\n\n

HREF (GPIO 1): Denotes the beginning of each line<\/p>\n\n\n\n

Y2 to Y9 (GPIOs 7, 3, 10, 4, 42, 5, 41, 6): Carry 8-bit pixel data<\/p>\n\n\n\n

SDA (GPIO 8) and SCL (GPIO 9): I\u00b2C communication lines for camera setup<\/p>\n\n\n\n

RESET (GPIO 2): Resets the camera module when required<\/p>\n\n\n\n

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