Most trail cameras flood you with useless shots — waving leaves, empty frames, the neighbor’s dog. Reviewing gigabytes of junk is boring, batteries die too fast, and cellular fees pile up.
Here’s what typically happens from the user’s perspective:
A wildlife researcher (or hunter, or landowner) sets up a camera deep in the forest. One or two weeks later, they return to check it. The SD card is full — thousands of images. They scroll through: moving leaves, empty frames, raccoons, deer, a dog, a hiker, more empty frames. Somewhere in there might be the one animal they were looking for.
Batteries are often dead. If the camera uses cellular transmission, that’s another battery drain — and a monthly subscription cost. And if there’s no signal? There’s no remote access at all. Reviewing footage takes hours. Most users get used to deleting 90% of the captured media.
We thought: why not add brains to the camera itself?
That’s how this project was born. Using the GRC AI DevBoard (Himax HX6538 + Ethos-U55), we built a pocket-sized demo that recognizes animals locally, in real time, with no cloud or internet.
This isn’t a concept. It’s hardware you can build and try right now — and it’s the perfect starting point for your own AI-powered trail camera.
Why it’s Awesome
- AI at the edge: no servers, no monthly plans, just on-board inference.
- Smarter than motion: filter out wind and rain, trigger only when a real animal appears.
- Energy-friendly: run for weeks on a single battery pack.
- Hands-on demo: transparent case shows exactly how the system works.
- Built on GRC AI DevBoard: our ready-to-use platform for embedded vision projects.
- AI Trail Camera — only capture the species you care about.
- Smart Bird Feeder — ID visitors and log their activity.
- Educational AI Kit — demonstrate neural networks live in a classroom.
- Prototype Platform — base for smart monitoring devices, research tools, or consumer products.
1. A camera module captures frames continuously (or can be ToF-triggered).
2. The image is processed on-device by a preloaded neural network.
3. Results appear instantly on a 320×240 TFT display(animal name, image, description).
In a real trail camera, this logic could instead trigger photo/video or control a high-res camera — only if the detected object is on your whitelist.
What You’ll Need- GRC AI DevBoard (HX6538)
- Camera module (MIPI/OV2640)
- TFT display 320×240
- Acrylic enclosure (transparent recommended, G214C or G212C)
- Power supply (USB-C or Li-ion)
- Cables & connectors
- Plastic animal figures (for testing)
You can use any small enclosure that fits your components — ideally something you already have on hand.
We used a box with a transparent lid, so it’s easy to see what's inside and how the system works.
- Mount the GRC AI DevBoard on the base using standoffs or adhesive pads
- Leave space for the camera, screen, and optional ToF sensor on the front
- Make openings (if needed) for the camera lens and screen
Make sure you can reach the USB-C port or battery connector for power.
Connect the ComponentsUsing jumper wires or pre-soldered headers:
- Connect the camera module (e.g., OV2640 or MIPI variant) to the camera connector on the dev board
- Connect the TFT display (320×240) to the display header
Soldering Wires to the DevBoard
· To connect the TFT display, you’ll first need to solder five wires to the exposed test pads TP1 to TP5on the GRC AI DevBoard.
On our assembled demo unit, the wires are connected as follows:
- TP1 → Black wire → GND (TFT display)
- TP2 → Yellow wire → SCK (TFT display)
- TP3 → Brown wire → CS (TFT display)
- TP4 → White wire → DC (TFT display)
- TP5 → Green wire → SDI / MOSI (TFT display)
Tip: Use thin, flexible wires and a fine-tip soldering iron to avoid bridging the pads.
In addition to the previous five wires, you’ll also need to solder two more wires to specific pads near the microSD card slot on the board.
Pad numbering starts from the USB Type-C connector side.
- Pad 5 → Orange wire → RESET (TFT display)
- Pad 6 → Red wire → VCC (TFT display)
Connecting the MIPI Camera
Now connect the MIPI camera module using the flat ribbon cable.
1. On the camera side: Gently lift the latch on the camera connector, insert the ribbon cable, and close the latch to secure it.
2. On the DevBoard side (connector J5): Lift the black latch on the J5 connector.
Insert the other end of the ribbon cable — make sure the blue marking on the cable is facing up. Firmly close the latch to lock the cable in place.
Connecting the TFT Display
Next, connect the soldered wires from the DevBoard to the corresponding pins on the TFT display.
On our demo unit, the connections are as follows:
- Red wire → VCC and LED pins (power supply for the display and backlight)
- Black wire → GND
- Brown wire → CS (Chip Select)
- Orange wire → RESET
- White wire → DC (Data/Command)
- Green wire → SDI / MOSI (data input)
- Yellow wire → SCK (clock)
Double-check pin labels on your specific TFT module — pin order may vary slightly between displays.
Mounting Inside the Enclosure
To secure the components inside the enclosure, we used a 3D-printed bracket to mount the DevBoard with the connected camera and TFT display.
This is just the beginning. You can:
- Swap the neural network for your own (e.g. specific species, pets, or humans).
- Add logic for video recording, SD storage, or wireless alerts.
- Adapt hardware for outdoor enclosures with solar charging.
All on the same GRC AI DevBoard. Once you have the board, you can run countless AI vision demos — from toys to real monitoring devices.
Share Your FeedbackTried it? Built your own version?
- Share your experience in the comments.
- Got a cool use case in mind? Let us know.
- Thinking about a commercial product? Reach out — we’d love to collaborate.
Related Hackster projects: AI Teacher | Gate | RoboControl | VoicePIN
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