TY - GEN
T1 - Visual hull reconstruction for automated primate behavior observation
AU - Ghadar, Nastaran
AU - Zhang, Xikang
AU - Li, Kang
AU - Erdogmus, Deniz
AU - Thibault, Guillaume
AU - Bayestehtashk, Alireza
AU - Coleman, Izhak Shafran Kris
AU - Grant, Kathleen A.
PY - 2013
Y1 - 2013
N2 - The study of social animal interactions is used as means for understanding animal behavior and biology. In this work, we describe a computerized method that utilizes 3D visual hull reconstruction to identify and localize rhesus macaques in their social groups. There are three major steps in this study. First, we collect experimental data from four synchronized cameras at different locations and angels in a cage containing five rhesus macaques. Second, by using computer vision algorithms, we detect and identify animals using 2D observations that were provided from the previous step. This provides essential quantitative data for animal behavior research. Finally, by applying visual hull reconstruction algorithm, we automatically build a 3D model for each rhesus macaques on every frame. The results of this work can be used for tracking these animals in their cage, and furthermore it can be used for activity recognition of social interactions of rhesus macaques. The method we developed in this paper, shows promising results that are accurate, yet runs in a timely manner; this makes this algorithm suitable for large datasets and we can use it for future high-level recognition tasks.
AB - The study of social animal interactions is used as means for understanding animal behavior and biology. In this work, we describe a computerized method that utilizes 3D visual hull reconstruction to identify and localize rhesus macaques in their social groups. There are three major steps in this study. First, we collect experimental data from four synchronized cameras at different locations and angels in a cage containing five rhesus macaques. Second, by using computer vision algorithms, we detect and identify animals using 2D observations that were provided from the previous step. This provides essential quantitative data for animal behavior research. Finally, by applying visual hull reconstruction algorithm, we automatically build a 3D model for each rhesus macaques on every frame. The results of this work can be used for tracking these animals in their cage, and furthermore it can be used for activity recognition of social interactions of rhesus macaques. The method we developed in this paper, shows promising results that are accurate, yet runs in a timely manner; this makes this algorithm suitable for large datasets and we can use it for future high-level recognition tasks.
KW - Visual hull reconstruction
KW - background substraction
KW - object detection
KW - social animals
UR - http://www.scopus.com/inward/record.url?scp=84893322357&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893322357&partnerID=8YFLogxK
U2 - 10.1109/MLSP.2013.6661922
DO - 10.1109/MLSP.2013.6661922
M3 - Conference contribution
AN - SCOPUS:84893322357
SN - 9781479911806
T3 - IEEE International Workshop on Machine Learning for Signal Processing, MLSP
BT - 2013 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2013
T2 - 2013 16th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2013
Y2 - 22 September 2013 through 25 September 2013
ER -