IEICE TRANSACTIONS on Information
Robust Motion Tracking of Multiple Objects with KL-IMMPDAF
Summary :
This paper describes how the image sequences taken by a stationary video camera may be effectively processed to detect and track moving objects against a stationary background in real-time. Our approach is first to isolate the moving objects in image sequences via a modified adaptive background estimation method and then perform token tracking of multiple objects based on features extracted from the processed image sequences. In feature based multiple object tracking, the most prominent tracking issues are track initialization, data association, occlusions due to traffic congestion, and object maneuvering. While there are limited past works addressing these problems, most relevant tracking systems proposed in the past are independently focused to either "occlusion" or "data association" only. In this paper, we propose the KL-IMMPDA (Kanade Lucas-Interacting Multiple Model Probabilistic Data Association) filtering approach for multiple-object tracking to collectively address the key issues. The proposed method essentially employs optical flow measurements for both detection and track initialization while the KL-IMMPDA filter is used to accept or reject measurements, which belong to other objects. The data association performed by the proposed KL-IMMPDA results in an effective tracking scheme, which is robust to partial occlusions and image clutter of object maneuvering. The simulation results show a significant performance improvement for tracking multi-objects in occlusion and maneuvering, when compared to other conventional trackers such as Kalman filter.
- Publication
- IEICE TRANSACTIONS on Information Vol.E84-D No.1 pp.179-187
- Publication Date
- 2001/01/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Image Processing, Image Pattern Recognition
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Jungduk SON, Hanseok KO, "Robust Motion Tracking of Multiple Objects with KL-IMMPDAF" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 1, pp. 179-187, January 2001, doi: .
Abstract: This paper describes how the image sequences taken by a stationary video camera may be effectively processed to detect and track moving objects against a stationary background in real-time. Our approach is first to isolate the moving objects in image sequences via a modified adaptive background estimation method and then perform token tracking of multiple objects based on features extracted from the processed image sequences. In feature based multiple object tracking, the most prominent tracking issues are track initialization, data association, occlusions due to traffic congestion, and object maneuvering. While there are limited past works addressing these problems, most relevant tracking systems proposed in the past are independently focused to either "occlusion" or "data association" only. In this paper, we propose the KL-IMMPDA (Kanade Lucas-Interacting Multiple Model Probabilistic Data Association) filtering approach for multiple-object tracking to collectively address the key issues. The proposed method essentially employs optical flow measurements for both detection and track initialization while the KL-IMMPDA filter is used to accept or reject measurements, which belong to other objects. The data association performed by the proposed KL-IMMPDA results in an effective tracking scheme, which is robust to partial occlusions and image clutter of object maneuvering. The simulation results show a significant performance improvement for tracking multi-objects in occlusion and maneuvering, when compared to other conventional trackers such as Kalman filter.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_1_179/_p
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@ARTICLE{e84-d_1_179,
author={Jungduk SON, Hanseok KO, },
journal={IEICE TRANSACTIONS on Information},
title={Robust Motion Tracking of Multiple Objects with KL-IMMPDAF},
year={2001},
volume={E84-D},
number={1},
pages={179-187},
abstract={This paper describes how the image sequences taken by a stationary video camera may be effectively processed to detect and track moving objects against a stationary background in real-time. Our approach is first to isolate the moving objects in image sequences via a modified adaptive background estimation method and then perform token tracking of multiple objects based on features extracted from the processed image sequences. In feature based multiple object tracking, the most prominent tracking issues are track initialization, data association, occlusions due to traffic congestion, and object maneuvering. While there are limited past works addressing these problems, most relevant tracking systems proposed in the past are independently focused to either "occlusion" or "data association" only. In this paper, we propose the KL-IMMPDA (Kanade Lucas-Interacting Multiple Model Probabilistic Data Association) filtering approach for multiple-object tracking to collectively address the key issues. The proposed method essentially employs optical flow measurements for both detection and track initialization while the KL-IMMPDA filter is used to accept or reject measurements, which belong to other objects. The data association performed by the proposed KL-IMMPDA results in an effective tracking scheme, which is robust to partial occlusions and image clutter of object maneuvering. The simulation results show a significant performance improvement for tracking multi-objects in occlusion and maneuvering, when compared to other conventional trackers such as Kalman filter.},
keywords={},
doi={},
ISSN={},
month={January},}
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TY - JOUR
TI - Robust Motion Tracking of Multiple Objects with KL-IMMPDAF
T2 - IEICE TRANSACTIONS on Information
SP - 179
EP - 187
AU - Jungduk SON
AU - Hanseok KO
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2001
AB - This paper describes how the image sequences taken by a stationary video camera may be effectively processed to detect and track moving objects against a stationary background in real-time. Our approach is first to isolate the moving objects in image sequences via a modified adaptive background estimation method and then perform token tracking of multiple objects based on features extracted from the processed image sequences. In feature based multiple object tracking, the most prominent tracking issues are track initialization, data association, occlusions due to traffic congestion, and object maneuvering. While there are limited past works addressing these problems, most relevant tracking systems proposed in the past are independently focused to either "occlusion" or "data association" only. In this paper, we propose the KL-IMMPDA (Kanade Lucas-Interacting Multiple Model Probabilistic Data Association) filtering approach for multiple-object tracking to collectively address the key issues. The proposed method essentially employs optical flow measurements for both detection and track initialization while the KL-IMMPDA filter is used to accept or reject measurements, which belong to other objects. The data association performed by the proposed KL-IMMPDA results in an effective tracking scheme, which is robust to partial occlusions and image clutter of object maneuvering. The simulation results show a significant performance improvement for tracking multi-objects in occlusion and maneuvering, when compared to other conventional trackers such as Kalman filter.
ER -
English
