IEICE TRANSACTIONS on Information
Multiple Human Tracking Using an Omnidirectional Camera with Local Rectification and World Coordinates Representation
Summary :
Multiple human tracking is widely used in various fields such as marketing and surveillance. The typical approach associates human detection results between consecutive frames using the features and bounding boxes (position+size) of detected humans. Some methods use an omnidirectional camera to cover a wider area, but ID switch often occurs in association with detections due to following two factors: i) The feature is adversely affected because the bounding box includes many background regions when a human is captured from an oblique angle. ii) The position and size change dramatically between consecutive frames because the distance metric is non-uniform in an omnidirectional image. In this paper, we propose a novel method that accurately tracks humans with an association metric for omnidirectional images. The proposed method has two key points: i) For feature extraction, we introduce local rectification, which reduces the effect of background regions in the bounding box. ii) For distance calculation, we describe the positions in a world coordinate system where the distance metric is uniform. In the experiments, we confirmed that the Multiple Object Tracking Accuracy (MOTA) improved 3.3 in the LargeRoom dataset and improved 2.3 in the SmallRoom dataset.
- Publication
- IEICE TRANSACTIONS on Information Vol.E103-D No.6 pp.1265-1275
- Publication Date
- 2020/06/01
- Publicized
- 2020/04/10
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2019MVP0009
- Type of Manuscript
- Special Section PAPER (Special Section on Machine Vision and its Applications)
- Category
Authors
Hitoshi NISHIMURA
KDDI Research, Inc.,Nagoya University
Naoya MAKIBUCHI
KDDI Research, Inc.
Kazuyuki TASAKA
KDDI Research, Inc.
Yasutomo KAWANISHI
KDDI Research, Inc.,Nagoya University
Hiroshi MURASE
KDDI Research, Inc.,Nagoya University
Keyword
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Hitoshi NISHIMURA, Naoya MAKIBUCHI, Kazuyuki TASAKA, Yasutomo KAWANISHI, Hiroshi MURASE, "Multiple Human Tracking Using an Omnidirectional Camera with Local Rectification and World Coordinates Representation" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 6, pp. 1265-1275, June 2020, doi: 10.1587/transinf.2019MVP0009.
Abstract: Multiple human tracking is widely used in various fields such as marketing and surveillance. The typical approach associates human detection results between consecutive frames using the features and bounding boxes (position+size) of detected humans. Some methods use an omnidirectional camera to cover a wider area, but ID switch often occurs in association with detections due to following two factors: i) The feature is adversely affected because the bounding box includes many background regions when a human is captured from an oblique angle. ii) The position and size change dramatically between consecutive frames because the distance metric is non-uniform in an omnidirectional image. In this paper, we propose a novel method that accurately tracks humans with an association metric for omnidirectional images. The proposed method has two key points: i) For feature extraction, we introduce local rectification, which reduces the effect of background regions in the bounding box. ii) For distance calculation, we describe the positions in a world coordinate system where the distance metric is uniform. In the experiments, we confirmed that the Multiple Object Tracking Accuracy (MOTA) improved 3.3 in the LargeRoom dataset and improved 2.3 in the SmallRoom dataset.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019MVP0009/_p
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@ARTICLE{e103-d_6_1265,
author={Hitoshi NISHIMURA, Naoya MAKIBUCHI, Kazuyuki TASAKA, Yasutomo KAWANISHI, Hiroshi MURASE, },
journal={IEICE TRANSACTIONS on Information},
title={Multiple Human Tracking Using an Omnidirectional Camera with Local Rectification and World Coordinates Representation},
year={2020},
volume={E103-D},
number={6},
pages={1265-1275},
abstract={Multiple human tracking is widely used in various fields such as marketing and surveillance. The typical approach associates human detection results between consecutive frames using the features and bounding boxes (position+size) of detected humans. Some methods use an omnidirectional camera to cover a wider area, but ID switch often occurs in association with detections due to following two factors: i) The feature is adversely affected because the bounding box includes many background regions when a human is captured from an oblique angle. ii) The position and size change dramatically between consecutive frames because the distance metric is non-uniform in an omnidirectional image. In this paper, we propose a novel method that accurately tracks humans with an association metric for omnidirectional images. The proposed method has two key points: i) For feature extraction, we introduce local rectification, which reduces the effect of background regions in the bounding box. ii) For distance calculation, we describe the positions in a world coordinate system where the distance metric is uniform. In the experiments, we confirmed that the Multiple Object Tracking Accuracy (MOTA) improved 3.3 in the LargeRoom dataset and improved 2.3 in the SmallRoom dataset.},
keywords={},
doi={10.1587/transinf.2019MVP0009},
ISSN={1745-1361},
month={June},}
Copy
TY - JOUR
TI - Multiple Human Tracking Using an Omnidirectional Camera with Local Rectification and World Coordinates Representation
T2 - IEICE TRANSACTIONS on Information
SP - 1265
EP - 1275
AU - Hitoshi NISHIMURA
AU - Naoya MAKIBUCHI
AU - Kazuyuki TASAKA
AU - Yasutomo KAWANISHI
AU - Hiroshi MURASE
PY - 2020
DO - 10.1587/transinf.2019MVP0009
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2020
AB - Multiple human tracking is widely used in various fields such as marketing and surveillance. The typical approach associates human detection results between consecutive frames using the features and bounding boxes (position+size) of detected humans. Some methods use an omnidirectional camera to cover a wider area, but ID switch often occurs in association with detections due to following two factors: i) The feature is adversely affected because the bounding box includes many background regions when a human is captured from an oblique angle. ii) The position and size change dramatically between consecutive frames because the distance metric is non-uniform in an omnidirectional image. In this paper, we propose a novel method that accurately tracks humans with an association metric for omnidirectional images. The proposed method has two key points: i) For feature extraction, we introduce local rectification, which reduces the effect of background regions in the bounding box. ii) For distance calculation, we describe the positions in a world coordinate system where the distance metric is uniform. In the experiments, we confirmed that the Multiple Object Tracking Accuracy (MOTA) improved 3.3 in the LargeRoom dataset and improved 2.3 in the SmallRoom dataset.
ER -
English
