@conference {49317, title = {Honeybee detection and pose estimation using convolutional neural networks.}, booktitle = {RFIAP 2018}, year = {2018}, month = {2018 Jun 26}, abstract = {

The ability to automatize the analysis of video for monitoring animals and insects is of great interest for behavior science and ecology [1]. In particular, honeybees play a crucial role in agriculture as natural pollinators. However, recent studies has shown that phenomena such as colony collapse disorder are causing the loss of many colonies [2]. Due to the high number of interacting factors to explain these events, a multi-faceted analysis of the bees in their environment is required. We focus in our work in developing tools to help model and understand their behavior as individuals, in relation with the health and performance of the colony.

In this paper, we report the development of a new system for the detection, locali- zation and tracking of honeybee body parts from video on the entrance ramp of the colony. The proposed system builds on the recent advances in Convolutional Neu- ral Networks (CNN) for Human pose estimation and evaluates the suitability for the detection of honeybee pose as shown in Figure 1. This opens the door for novel animal behavior analysis systems that take advantage of the precise detection and tracking of the insect pose.\ 

}, url = {https://rfiap2018.ign.fr/sites/default/files/ARTICLES/RFIAP_2018/RFIAP_2018_Felipe_Honeybee.pdf}, author = {Rodriguez, Ivan F and Branson, Kristin M and Acuna, Edgar and Agosto-Rivera, Jose and Giray, Tugrul and Megret, Remi} } @conference {49417, title = {Multiple animals tracking in video using part affinity fields}, booktitle = {Visual observation and analysis of Vertebrate And Insect Behavior 2018}, year = {2018}, month = {2018 Aug 20}, abstract = {

In this work, we address the problem of pose detection and tracking of multiple individuals for the study of behaviour in insects and animals. Using a Deep Neural Network architecture, precise detection and association of the body parts can be performed. The models are learned based on user-annotated training videos, which gives flexibility to the approach. This is illustrated on two different animals: honeybees and mice, where very good performance in part recognition and association are observed despite the presence of multiple interacting individuals.

}, url = {http://homepages.inf.ed.ac.uk/rbf/VAIB18PAPERS/vaib18rodriguez.pdf}, author = {Rodriguez, Ivan F and Megret, Remi and Egnor, Roian and Branson, Kristin and Agosto, Jose L and Giray, Tugrul and Acuna, Edgar} }