TY - GEN
T1 - Data association based ant tracking with interactive error correction
AU - Nguyen, Hoan
AU - Fasciano, Thomas
AU - Charbonneau, Daniel
AU - Dornhaus, Anna
AU - Shin, Min C.
PY - 2014
Y1 - 2014
N2 - The tracking of ants in video is important for the analysis of their complex group behavior. However, the manual analysis of these videos is tedious and time consuming. Automated tracking methods tend to drift due to frequent occlusions during their interactions and similarity in appearance. Semi-automated tracking methods enable corrections of tracking errors by incorporating user interaction. Although it is much lower than manual analysis, the required user time of the existing method is still typically 23 times the actual video length. In this paper, we propose a new semi-automated method that achieves similar accuracy while reducing the user interaction time by (1) mitigating user wait time by incorporating a data association tracking method to separate the tracking from user correction, and (2) minimizing the number of candidates visualized for user during correction. This proposed method is able to reduce the user interaction time by 67% while maintaining the accuracy within 3% of the previous semi-automated method [11].
AB - The tracking of ants in video is important for the analysis of their complex group behavior. However, the manual analysis of these videos is tedious and time consuming. Automated tracking methods tend to drift due to frequent occlusions during their interactions and similarity in appearance. Semi-automated tracking methods enable corrections of tracking errors by incorporating user interaction. Although it is much lower than manual analysis, the required user time of the existing method is still typically 23 times the actual video length. In this paper, we propose a new semi-automated method that achieves similar accuracy while reducing the user interaction time by (1) mitigating user wait time by incorporating a data association tracking method to separate the tracking from user correction, and (2) minimizing the number of candidates visualized for user during correction. This proposed method is able to reduce the user interaction time by 67% while maintaining the accuracy within 3% of the previous semi-automated method [11].
UR - http://www.scopus.com/inward/record.url?scp=84904647434&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904647434&partnerID=8YFLogxK
U2 - 10.1109/WACV.2014.6836003
DO - 10.1109/WACV.2014.6836003
M3 - Conference contribution
AN - SCOPUS:84904647434
SN - 9781479949854
T3 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
SP - 941
EP - 946
BT - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
PB - IEEE Computer Society
T2 - 2014 IEEE Winter Conference on Applications of Computer Vision, WACV 2014
Y2 - 24 March 2014 through 26 March 2014
ER -