Caught on camera https://creativecommons.org/licenses/by-nc-sa/3.0/

Computers are playing spot the difference in the Serengeti. An image-recognition algorithm that can identify different species could make it easier to track animals in the wild.

Using a database of 3.2 million photos taken by hidden camera traps in the Serengeti National Park in Tanzania, Jeff Clune at the University of Wyoming in Laramie and his colleagues trained the deep-learning system to distinguish between 48 animal species, such as elephants, giraffes and gazelles. In tests, it correctly identified the species present in an image 92 per cent of the time.

Camera traps automatically take pictures of passing animals when triggered by heat and motion. This produces thousands or millions of photographs for ecologists to study, but people usually have to go through and label what each picture shows by hand, says Ali Swanson, who worked on the project while at the University of Oxford. If an algorithm could categorise at least some of the images, it could save a lot of time.


In 2010, Swanson set up 225 camera traps in the Serengeti, inviting an army of 70,000 online volunteers to help label the images. When Clune heard about this, he saw a perfect opportunity for deep learning – so he and Swanson arranged to team up on the project.

“Right now in AI and deep learning, one of the hardest things to come by is a very good, large labelled dataset,” says Clune.

His team started by teaching a neural network to recognise whether an image contained an animal, which 75 per cent of the Serengeti images lack. The researchers then trained it to differentiate between species.

Better ID

The system is much better at identifying the most common animals in the data set, such as wildebeest, says Clune. It has trouble with more rare species like the zorilla, a type of polecat that only appears in the images a few dozen times.

Clune says the system could be used to classify most of the photographs and researchers could work on any it wasn’t sure about. It could then be further trained on these hand-labelled images to get better at recognising rarer species. The team also plans to test whether the system can identify animal behaviour in images.

“This is very exciting,” says Chris Carbone at the Zoological Society of London. Automatic species recognition could help us learn more about the distribution of species and get a better idea of the impact humans are having on them, he says.

An ideal system would provide live tracking information about animals as they pass traps, says Swanson. But the challenge would be transmitting the data from the device in real time for the system to analyse, rather than the current method of using an SD card to store the data on the device until a researcher comes along to collect it.

One difficulty is that hyenas and elephants have a habit of damaging the cameras, which are thus kept in heavy-duty plastic cases with no space for an antenna that can transmit data. “And if you do put an antenna on a camera, it won’t last very long at all,” says Swanson. “Something will come along and chew it off pretty quick.”

Reference: arxiv.org/abs/1703.05830