Title :
Biotracking gives back to nature
Abstract :
We develop a biotracking software, which helps biologists track animals and insects and study their behavior by analyzing videotaped data. To biologists, whose observation and analysis methods are time-consuming, the automated system has been a boon. The idea for the biotracking software was conceived while studying robots. We use hidden Markov models (HMMs) to track the moving robots and extract the behaviors from their motion. The technology also has broader applications; it can be applied to any large group of observable and trackable moving agents. The program tracks objects of the ant color from video frame to video frame. The program makes it easy to view the bee activity logs and label the types of bee activity by combining HMM learning with k-nearest neighbor classification.
Keywords :
biology computing; hidden Markov models; learning (artificial intelligence); multi-agent systems; HMM learning; bee activity log; biotracking software; hidden Markov model; k-nearest neighbor classification; multiagent systems; videotaped data; Biological information theory; Biology; Books; Collaboration; Humans; Machine vision; Particle tracking; Profitability; Refrigeration; System testing;
Journal_Title :
Intelligent Systems, IEEE
DOI :
10.1109/MIS.2004.1265877