Abstract
This paper proposes the prediction based occluded multitarget tracking method using spatio-temporal attention mechanism. To cope with occlusion between targets, the proposed method provides an efficient method for more complex analysis by combining object association with partial probability model in spatially attentive window and occlusion activity detection in predicted temporal location. While multiple objects are moving or occluding between them in areas of visual field, a simultaneous tracking of multiple objects tends to fail. This is due to the fact that incompletely estimated feature vectors such as location, color, velocity, and acceleration of a target can provide only ambiguous and missing information. Thus, the spatially and temporally considered mechanism is proposed to track each target before, during, and after occlusion. Robustness of the proposed method is demonstrated with representative simulations.
| Original language | English |
|---|---|
| Pages (from-to) | 925-938 |
| Number of pages | 14 |
| Journal | International Journal of Pattern Recognition and Artificial Intelligence |
| Volume | 20 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 2006 Sep 1 |
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Keywords
- Focus of attention
- Multitarget tracking
- Occlusion
- Spatial attention
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Vision and Pattern Recognition
- Control and Systems Engineering
Cite this
Prediction based occluded multitarget tracking using spatio-temporal attention. / Lee, Heungkyu; Kim, June; Ko, Hanseok.
In: International Journal of Pattern Recognition and Artificial Intelligence, Vol. 20, No. 6, 01.09.2006, p. 925-938.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Prediction based occluded multitarget tracking using spatio-temporal attention
AU - Lee, Heungkyu
AU - Kim, June
AU - Ko, Hanseok
PY - 2006/9/1
Y1 - 2006/9/1
N2 - This paper proposes the prediction based occluded multitarget tracking method using spatio-temporal attention mechanism. To cope with occlusion between targets, the proposed method provides an efficient method for more complex analysis by combining object association with partial probability model in spatially attentive window and occlusion activity detection in predicted temporal location. While multiple objects are moving or occluding between them in areas of visual field, a simultaneous tracking of multiple objects tends to fail. This is due to the fact that incompletely estimated feature vectors such as location, color, velocity, and acceleration of a target can provide only ambiguous and missing information. Thus, the spatially and temporally considered mechanism is proposed to track each target before, during, and after occlusion. Robustness of the proposed method is demonstrated with representative simulations.
AB - This paper proposes the prediction based occluded multitarget tracking method using spatio-temporal attention mechanism. To cope with occlusion between targets, the proposed method provides an efficient method for more complex analysis by combining object association with partial probability model in spatially attentive window and occlusion activity detection in predicted temporal location. While multiple objects are moving or occluding between them in areas of visual field, a simultaneous tracking of multiple objects tends to fail. This is due to the fact that incompletely estimated feature vectors such as location, color, velocity, and acceleration of a target can provide only ambiguous and missing information. Thus, the spatially and temporally considered mechanism is proposed to track each target before, during, and after occlusion. Robustness of the proposed method is demonstrated with representative simulations.
KW - Focus of attention
KW - Multitarget tracking
KW - Occlusion
KW - Spatial attention
UR - http://www.scopus.com/inward/record.url?scp=33748988484&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33748988484&partnerID=8YFLogxK
U2 - 10.1142/S0218001406005058
DO - 10.1142/S0218001406005058
M3 - Article
AN - SCOPUS:33748988484
VL - 20
SP - 925
EP - 938
JO - International Journal of Pattern Recognition and Artificial Intelligence
JF - International Journal of Pattern Recognition and Artificial Intelligence
SN - 0218-0014
IS - 6
ER -