When Your Camera Thinks Before It Shoots

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.

Use Cases

• 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.

How it Works

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)

Build It Yourself

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 Components

Using 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.

Power and Final Assembly

• Power the system via USB-C or a Li-ion battery
• Route cables neatly to avoid stress or obstruction
• Close the enclosure — make sure the camera and screen align with their windows (if any)
• Power it up: the system should boot and display a welcome or idle screen

Flashing the Firmware

To run the demo, you’ll need to flash the prebuilt firmware onto the GRC AI DevBoard.

1. Download the Firmware

You can download the ready-to-use binary from our GitHub repository: https://github.com/Grovety/grc_ai_module_himax/tree/main/Wild_example

Licensed under CC BY-NC 4.0: free to use and modify non-commercially with attribution; commercial use requires permission (hi@grovety.com).

2. Flash the Firmware

1. Connect the board to your PC using a USB Type-C cable

2. Open Device Manager and find the COM port assigned to the board

3. Open the Command Prompt and navigate to the folder containing the flashing tool

4. Run the flashing command:

flasher_tool.exe --port <COM-port>

5. When the message Waiting for burn appears, press the SW1 button on the board (located on the side opposite the USB connector)

6. Wait until the process completes successfully

Troubleshooting

If nothing happens after pressing SW1, the flashing tool might be targeting the wrong USB side.

Try the following:

• Unplug the USB cable
• Flip the Type-C connector and reconnect it
• Check the new COM port in Device Manager
• Repeat steps 3–6

Check Inference Output

You can monitor real-time inference using the serial terminal:

1. Open Command Prompt and navigate to the firmware folder

2. Set up Python environment and install pyserial:

python -m venv env
env\Scripts\activate
python -m pip install pyserial

3. Start the serial monitor:

python -m serial.tools.miniterm <COM-port> 921600

You’ll see lines like:

label: elephant
category: 4
prob: 0.87

• label – name of the detected animal (if any)
• category – numeric ID corresponding to the detected animal class
• prob – confidence score (probability) for the detected object

Category list:

0 – bear 1 – cat 2 – cow 3 – dog 4 – elephant 5 – giraffe 6 – horse 7 – zebra

If instead you see Booting nRF Connect SDK, unplug and flip the USB Type-C cable again, then retry.

What’s Next

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.

Want to Use Your Own Model?

We initially planned to provide a public API for swapping the neural network — so you could retrain the system to detect other objects or animal species.

This feature is still in development.
However, if you're interested, feel free to reach out: hi@grovety.com
Or send us a direct message here on Hackster.

We’ll be happy to guide you through the process — or even help build a custom model tailored to your use case.

Share Your Feedback

Tried 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.

Links

• Buy GRC AI DevBoard on Elecrow
• Firmware & Code on GitHub
• Download STL: 3D-printed bracket for assembly

Related Hackster projects: AI Teacher | Gate | RoboControl | VoicePIN