Projects
Blyzer
A digital platform for automatic analysis of animal behavior based on video, sensor and audio data.
DOGFLW: DOG Facial Landmarks in the Wild Dataset
The Dog Facial Landmarks in the Wild (DogFLW) dataset offers a set od dog images in various environments and conditions, each annotated with 46 facial landmarks and a bounding box on the dogs' face. This dataset is designed to aid researchers in analyzing dog emotions and behaviors.
petsdatalab
Citizen science is becoming more popular in animal behavior research. petsDATAlab is a platform that allows researchers to quickly create mobile apps for data collection through pet owners. This eliminates the need for expert developers and designers and reduces the time required to build the app from months to just a few clicks.
Emotional Dog Faces Dataset
Recognizing emotional states in animals is important, but identifying animal affect is challenging for dogs. This research project presents the first dataset of dog faces in positive and negative emotions, collected through a controlled experiment, to address this gap. The dataset contains dog faces cut from video clips of 29 Labrador Retrievers in two emotional states: Negative (frustration) and Positive (anticipation).
The DogAge Project
This project aims to develop AI models for estimating dog age, a task that has been overlooked despite the fact that dogs are the most well-studied species in animal science and their ageing processes are similar to those of humans. To achieve this, a large-scale, crowdsourced data collection is being launched, and dog owners are invited to contribute photos of their pets.
BOVINETALK
There is a need to develop non-invasive animal-based indicators of affective states in livestock species, such as using vocal indicators. Cows produce LF calls for close distance contacts and HF calls for long distance communication, the latter being associated with negative affective states. We present an open-source dataset in project page.
Unveiling Canine Behavior Through Sensor Fusion
Recent advances in deep learning have improved our ability to track animal behavior in labs. Applying these techniques to larger animals in open spaces, like dogs, is challenging. We're bridging this gap by using wearable sensors to monitor and analyze dog behavior in various real-world situations.