Chasing Cats


This project was motivated by two things: a desire to learn more about neural net software and a desire to encourage the neighbors' cats to hang out somewhere other than my front yard.

The project includes just three hardware components: an Nvidia Jetson TX1 board, a Foscam FI9800P IP camera, and a Particle Photon attached to a relay The camera is mounted on the side of the house aimed at the front yard. It communicates with a WIFI Access Point maintained by the Jetson sitting in the den near the front yard. The Photon and relay are mounted in the control box for my sprinkler system and are connected to a WIFI access point in the kitchen.

In operation, the camera is configured to watch for changes in the yard. When something changes, the camera FTPs a set of 7 images, one per second, to the Jetson. A service running on the Jetson watches for inbound images, passing them to the Caffe deep learning neural network. If a cat is detected by the network, the Jetson signals the Photon's server in the cloud, which sends a message to the Photon. The Photon responds by turning on the sprinklers for two minutes.

Here, a cat wandered into the frame triggering the camera:

Cat Image

The cat made it out to the middle of the yard a few seconds later, triggering the camera again, just as the sprinklers popped up:

Cat Image

Installing the camera

There was nothing unusual about installing the camera. The only permanent connection is a 12 volt wire that comes through a small hole under the eaves. I mounted the camera on a wooden box positioned to show the front yard. There are a bunch of wires attached to the camera that I hid in the box as well. Here's a shot of the camera:

Camera Image

Follow the directions from Foscam to associate it with the Jetson's AP (see below). In my setup the Jetson is at I gave the camera a fixed IP address,, to make it easy to find. Once that is done, connect your Windows laptop to the camera and configure the "Alert" setting to trigger on a change. Set the system up to FTP 7 images on an alert. Then give it a user ID and password on the Jetson. My camera sends 640x360 images, FTPing them to its home directory.

Here are some selected configuration parameters for the camera:

Section Parameter Value
Network: IP Configuration
Obtain IP from DHCP Not checked
IP Address
Network: FTP Settings
FTP Server
Port 21
Username Jetson_login_name_for_camera
Password Jetson_login_password_for_camera
Video Snapshot Settings
Pictures Save To FTP
Enable timing to capture Not checked
Enable Set Filename Not checked
Detector Motion Detection
Trigger Interval 7s
Take Snapshot Checked
Timer Interval 1s

Installing the Particle Photon

The Photon was easy to setup. See the software section below for the code. It sits in the irrigation control box like this:

Photon Image

The black box on the left with the blue LED is a 24VAC to 5VDC converter from EBay. You can see the white relay on the relay board and the blue connector on the front. The Photon board is on the right. Both are taped to a piece of perfboard to hold them together.

The converter's 5V output is connected to the Photon's VIN pin. The relay board is basically analog: it has an open-collector NPN transistor with a nominal 3.3V input to the transistor's base and a 3V relay. The Photon's regulator could not supply enough current to drive the relay so I connected the collector of the input transistor to 5V through a 15 ohm 1/2 watt current limiting resistor. The relay contacts are connected to the water valve in parallel with the normal control circuit.

Here's the way it is wired:

24VAC converter 24VAC<--->Control box 24VAC OUT
24VAC converter +5V<--->Photon VIN, resistor to relay board +3.3V
24VAC converter GND<--->Photon GND, Relay GND
Photon D0<--->Relay board signal input
Relay COM<--->Control box 24VAC OUT
Relay NO<--->Front yard water valve

Installing the Jetson

Jetson Image

The only hardware bits added to the Jetson are a SATA SSD drive and a small Belkin USB hub. The hub has two wireless dongles that talk to the keyboard and mouse.

The SSD came up with no problem. I reformatted it as EXT4 and mounted it as /caffe. I strongly recommend moving all of your project code, git repos and application data off of the Jetson's internal SD card because it is often easiest to wipe the system during Jetpack upgrades.

The Wireless Access Point setup was pretty easy (really!) if you follow this guide. Just use the Ubuntu menus as directed and make sure you add this config setting.

I installed vsftpd as the FTP server. The configuration is pretty much stock. I did not enable anonymous FTP. I gave the camera a login name and password that is not used for anything else.

I installed Caffe using the JetsonHacks recipe. I believe current releases no longer have the LMDB_MAP_SIZE issue so try building it before you make the change. You should be able to run the tests and the timing demo mentioned in the JetsonHacks installation shell script. I'm currently using Cuda 7.0 but I'm not sure that it matters much at this stage. Do use CDNN, it saves a substantial amount of memory on these small systems. Once it is built add the build directory to your PATH variable so the scripts can find Caffe. Also add the Caffe Python lib directory to your PYTHONPATH.

~ $ echo $PATH
~ $ echo $PYTHONPATH

I'm running a variant of the Fully Convolutional Network for Semantic Segmentation (FCN). See the Berkeley Model Zoo, github.

I tried several other networks and finally ended up with FCN. More on on the selection process in a future article. Fcn32s runs well on the TX1 - it takes a bit more than 1GB of memory, starts up in about 10 seconds, and segments a 640x360 image in about a third of a second. The current github repo has a nice set of scripts, and the setup is agnostic about the image size - it resizes the net to match whatever you throw at it.

To give it a try, you will need to pull down a pre-trained Caffe models. This will take a few minutes: fcn32s-heavy-pascal.caffemodel is over 500MB.

    $ cd voc-fcn32s
    $ wget `cat caffemodel-url`

Edit, changing the path in the command to a valid .jpg. Change the "net" line to point to the model you just downloaded:

    -net = caffe.Net('fcn8s/deploy.prototxt', 'fcn8s/fcn8s-heavy-40k.caffemodel', caffe.TEST)
    +net = caffe.Net('voc-fcn32s/deploy.prototxt', 'voc-fcn32s/fcn32s-heavy-pascal.caffemodel', caffe.TEST)

You will need the file voc-fcn32s/deploy.prototxt. It is easily generated from voc-fcn32s/train.prototxt. Look at the changes between voc-fcn8s/train.prototxt and voc-fcn8s/deploy.prototxt to see how to do it, or you can get it from my chasing-cats github repo. You should now be able to run

    $ python

My repo includes several versions of, a few Python utilities that know about segmented files, the Photon code and control scripts and the operational scripts I use to run and monitor the system. More on software in the next post, Network Selection and Training.

Contact me at for questions or comments.