Visual Computing

University of Konstanz
2013 IEEE International Conference on Image Processing

Automatic framework for tracking honeybeeś antennae and mouthparts from low framerate video

M. Shen, P. Szyszka, C. Galizia, D. Merhof


Automatic tracking of the movement of bee’s antennae and mouthparts is necessary for studying associative learning of individuals. However, the problem of tracking them is challenging: First, the different classes of objects possess similar appearance and are close to each other. Second, tracking gaps are often present, due to the low frame-rate of the acquired video and the fast motion of the objects. Most existing insect tracking approaches have been developed for slow moving objects, and are not suitable for this application. In this paper, a novel Bayesian framework is proposed to automatically track bees’ antennae and their mouthparts. This framework incorporates information about their kinematics, shape, order and temporal correlation between neighboring frames. Experimental evaluation demonstrates the effectiveness and efficiency of the proposed framework.


  author     = {M. Shen and P. Szyszka and C. Galizia and D. Merhof},
  booktitle  = {2013 IEEE International Conference on Image Processing},
  doi        = {10.1109/ICIP.2013.6738847},
  issn       = {1522-4880},
  keywords   = {object detection;target tracking;Bayesian framework;associative learning;automatic framework;automatic tracking;automatically track bees antennae;honeybee mouthpart tracking;honeybee mouthparts tracking;insect tracking approaches;low frame-rate video;neighboring frames;object detection;slow moving objects;temporal correlation;tracking gaps;tracking problem;bee antennae and mandibles and proboscis;merged detections;multi-target tracking;splitted detections},
  month      = {sep},
  pages      = {4112-4116},
  title      = {Automatic framework for tracking honeybeeś antennae and mouthparts from low framerate video},
  year       = {2013},

Supplemental Material

Paper (.pdf, 119.1 KB)