The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Recentemente, o desempenho dos rastreadores do filtro de correlação discriminativa (CF) está cada vez melhor no rastreamento visual. Neste artigo, propomos regularização espaço-temporal com estimativa precisa de estado baseada em filtro de correlação discriminativa (STPSE) para obter um desempenho de rastreamento mais significativo. Primeiro, consideramos a mudança contínua do estado do objeto, utilizando as informações dos dois filtros anteriores para treinar o modelo de filtro de correlação. Aqui, treinamos o modelo de filtro de correlação com os recursos artesanais. Em segundo lugar, introduzimos o controle de atualização no qual a energia média do pico à correlação (APCE) e a distância entre as localizações dos objetos obtidas por recursos HOG e recursos artesanais são utilizados para detectar anormalidades no estado ao redor do objeto. APCE e a distância indicam a confiabilidade da resposta do filtro, portanto caso seja detectada anormalidade, o método proposto não atualiza a escala e a localização do objeto estimada pela resposta do filtro. No experimento, nosso rastreador (STPSE) atinge desempenho significativo e em tempo real apenas com CPU para a desafiadora sequência de benchmark (OTB2013, OTB2015 e TC128).
Zhaoqian TANG
Meiji University
Kaoru ARAKAWA
Meiji University
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Zhaoqian TANG, Kaoru ARAKAWA, "Spatial-Temporal Regularized Correlation Filter with Precise State Estimation for Visual Tracking" in IEICE TRANSACTIONS on Fundamentals,
vol. E105-A, no. 6, pp. 914-922, June 2022, doi: 10.1587/transfun.2021EAP1087.
Abstract: Recently, the performances of discriminative correlation filter (CF) trackers are getting better and better in visual tracking. In this paper, we propose spatial-temporal regularization with precise state estimation based on discriminative correlation filter (STPSE) in order to achieve more significant tracking performance. First, we consider the continuous change of the object state, using the information from the previous two filters for training the correlation filter model. Here, we train the correlation filter model with the hand-crafted features. Second, we introduce update control in which average peak-to-correlation energy (APCE) and the distance between the object locations obtained by HOG features and hand-crafted features are utilized to detect abnormality of the state around the object. APCE and the distance indicate the reliability of the filter response, thus if abnormality is detected, the proposed method does not update the scale and the object location estimated by the filter response. In the experiment, our tracker (STPSE) achieves significant and real-time performance with only CPU for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2021EAP1087/_p
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@ARTICLE{e105-a_6_914,
author={Zhaoqian TANG, Kaoru ARAKAWA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Spatial-Temporal Regularized Correlation Filter with Precise State Estimation for Visual Tracking},
year={2022},
volume={E105-A},
number={6},
pages={914-922},
abstract={Recently, the performances of discriminative correlation filter (CF) trackers are getting better and better in visual tracking. In this paper, we propose spatial-temporal regularization with precise state estimation based on discriminative correlation filter (STPSE) in order to achieve more significant tracking performance. First, we consider the continuous change of the object state, using the information from the previous two filters for training the correlation filter model. Here, we train the correlation filter model with the hand-crafted features. Second, we introduce update control in which average peak-to-correlation energy (APCE) and the distance between the object locations obtained by HOG features and hand-crafted features are utilized to detect abnormality of the state around the object. APCE and the distance indicate the reliability of the filter response, thus if abnormality is detected, the proposed method does not update the scale and the object location estimated by the filter response. In the experiment, our tracker (STPSE) achieves significant and real-time performance with only CPU for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).},
keywords={},
doi={10.1587/transfun.2021EAP1087},
ISSN={1745-1337},
month={June},}
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TY - JOUR
TI - Spatial-Temporal Regularized Correlation Filter with Precise State Estimation for Visual Tracking
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 914
EP - 922
AU - Zhaoqian TANG
AU - Kaoru ARAKAWA
PY - 2022
DO - 10.1587/transfun.2021EAP1087
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E105-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2022
AB - Recently, the performances of discriminative correlation filter (CF) trackers are getting better and better in visual tracking. In this paper, we propose spatial-temporal regularization with precise state estimation based on discriminative correlation filter (STPSE) in order to achieve more significant tracking performance. First, we consider the continuous change of the object state, using the information from the previous two filters for training the correlation filter model. Here, we train the correlation filter model with the hand-crafted features. Second, we introduce update control in which average peak-to-correlation energy (APCE) and the distance between the object locations obtained by HOG features and hand-crafted features are utilized to detect abnormality of the state around the object. APCE and the distance indicate the reliability of the filter response, thus if abnormality is detected, the proposed method does not update the scale and the object location estimated by the filter response. In the experiment, our tracker (STPSE) achieves significant and real-time performance with only CPU for the challenging benchmark sequence (OTB2013, OTB2015, and TC128).
ER -