DocumentCode
384249
Title
Tracking multiple animals in wildlife footage
Author
Tweed, David ; Calway, Andrew
Author_Institution
Dept. of Comput. Sci., Bristol Univ., UK
Volume
2
fYear
2002
fDate
2002
Firstpage
24
Abstract
We describe a method for tracking animals in wildlife footage. It uses a CONDENSATION particle filtering frame-work driven by learnt characteristics of specific animals. The key contribution is a periodic model of animal motion based on the relative positions over time of trackable features at significant body points. We also introduce techniques for maintaining a multimodal state density within the particle filter over time to enable consistent tracking of multiple animals. Initial experiments show that the approach has considerable potential.
Keywords
filtering theory; image motion analysis; image sequences; object detection; optical tracking; video signal processing; CONDENSATION particle filtering framework; animal characteristics; body points; multimodal state density; multiple animal tracking; periodic animal motion model; trackable features; video; wildlife footage; Animals; Computer science; Data mining; Focusing; Image sequences; Layout; Particle tracking; Probability distribution; Robustness; Wildlife;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 2002. Proceedings. 16th International Conference on
ISSN
1051-4651
Print_ISBN
0-7695-1695-X
Type
conf
DOI
10.1109/ICPR.2002.1048227
Filename
1048227
Link To Document