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Object tracking

HOW VIDEO OBJECT TRACKING IS AFFECTED BY IN-CAPTURE DISTORTIONS?


Video Object Tracking -VOT- in realistic scenarios is a difficult task. Image factors such as occlusion, clutter, confusion, object shape, and zooming, among others, have an impact on video tracker methods performance. While these conditions do affect trackers performance, there is not a clear distinction between the scene content challenges like occlusion and clutter, against challenges due to distortions generated by capture, compression, processing, and transmission of videos. This paper is concerned with the latter interpretation of quality as it affects VOT performance.

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Authors:
Hernan Dario Benitez Restrepo, Ivan Cabezas
Submitted On:
7 May 2019 - 8:25pm
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[1] Hernan Dario Benitez Restrepo, Ivan Cabezas, "HOW VIDEO OBJECT TRACKING IS AFFECTED BY IN-CAPTURE DISTORTIONS?", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3973. Accessed: Sep. 15, 2019.
@article{3973-19,
url = {http://sigport.org/3973},
author = {Hernan Dario Benitez Restrepo; Ivan Cabezas },
publisher = {IEEE SigPort},
title = {HOW VIDEO OBJECT TRACKING IS AFFECTED BY IN-CAPTURE DISTORTIONS?},
year = {2019} }
TY - EJOUR
T1 - HOW VIDEO OBJECT TRACKING IS AFFECTED BY IN-CAPTURE DISTORTIONS?
AU - Hernan Dario Benitez Restrepo; Ivan Cabezas
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3973
ER -
Hernan Dario Benitez Restrepo, Ivan Cabezas. (2019). HOW VIDEO OBJECT TRACKING IS AFFECTED BY IN-CAPTURE DISTORTIONS?. IEEE SigPort. http://sigport.org/3973
Hernan Dario Benitez Restrepo, Ivan Cabezas, 2019. HOW VIDEO OBJECT TRACKING IS AFFECTED BY IN-CAPTURE DISTORTIONS?. Available at: http://sigport.org/3973.
Hernan Dario Benitez Restrepo, Ivan Cabezas. (2019). "HOW VIDEO OBJECT TRACKING IS AFFECTED BY IN-CAPTURE DISTORTIONS?." Web.
1. Hernan Dario Benitez Restrepo, Ivan Cabezas. HOW VIDEO OBJECT TRACKING IS AFFECTED BY IN-CAPTURE DISTORTIONS? [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3973

DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT


Visual tracking frameworks employing Convolutional Neural Networks (CNNs) have shown state-of-the-art performance due to their hierarchical feature representation. While classification and update based deep neural net tracking have shown good performance in terms of accuracy, they have poor tracking speed. On the other hand, recent matching based techniques using CNNs show higher than real-time speed in tracking but this speed is achieved at a considerably lower accuracy.

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6 October 2018 - 9:51pm
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[1] , "DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3581. Accessed: Sep. 15, 2019.
@article{3581-18,
url = {http://sigport.org/3581},
author = { },
publisher = {IEEE SigPort},
title = {DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT},
year = {2018} }
TY - EJOUR
T1 - DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3581
ER -
. (2018). DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT. IEEE SigPort. http://sigport.org/3581
, 2018. DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT. Available at: http://sigport.org/3581.
. (2018). "DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT." Web.
1. . DEEP MATCH TRACKER: CLASSIFYING WHEN DISSIMILAR, SIMILARITY MATCHING WHEN NOT [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3581

Gate connected Convolutional Neural Network for Object Tracking


Convolutional neural networks (CNNs) have been employed in visual tracking due to their rich levels of feature representation. While the learning capability of a CNN increases with its depth, unfortunately spatial information is diluted in deeper layers which hinders its important ability to localise targets. To successfully manage this trade-off, we propose a novel residual network based gating CNN architecture for object tracking. Our deep model connects the front and bottom convolutional features with a gate layer.

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Authors:
T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi
Submitted On:
11 September 2017 - 6:09pm
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[1] T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi, "Gate connected Convolutional Neural Network for Object Tracking", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1919. Accessed: Sep. 15, 2019.
@article{1919-17,
url = {http://sigport.org/1919},
author = {T.Kokul; C.Fookes; S.Sridharan; A.Ramanan; U.A.J.Pinidiyaarachchi },
publisher = {IEEE SigPort},
title = {Gate connected Convolutional Neural Network for Object Tracking},
year = {2017} }
TY - EJOUR
T1 - Gate connected Convolutional Neural Network for Object Tracking
AU - T.Kokul; C.Fookes; S.Sridharan; A.Ramanan; U.A.J.Pinidiyaarachchi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1919
ER -
T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi. (2017). Gate connected Convolutional Neural Network for Object Tracking. IEEE SigPort. http://sigport.org/1919
T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi, 2017. Gate connected Convolutional Neural Network for Object Tracking. Available at: http://sigport.org/1919.
T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi. (2017). "Gate connected Convolutional Neural Network for Object Tracking." Web.
1. T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi. Gate connected Convolutional Neural Network for Object Tracking [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1919

Gate connected Convolutional Neural Network for Object Tracking


Convolutional neural networks (CNNs) have been employed in visual tracking due to their rich levels of feature representation. While the learning capability of a CNN increases with its depth, unfortunately spatial information is diluted in deeper layers which hinders its important ability to localise targets. To successfully manage this trade-off, we propose a novel residual network based gating CNN architecture for object tracking. Our deep model connects the front and bottom convolutional features with a gate layer.

Paper Details

Authors:
T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi
Submitted On:
11 September 2017 - 6:09pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
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Cite

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poster_ICIP.pdf

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[1] T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi, "Gate connected Convolutional Neural Network for Object Tracking", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1918. Accessed: Sep. 15, 2019.
@article{1918-17,
url = {http://sigport.org/1918},
author = {T.Kokul; C.Fookes; S.Sridharan; A.Ramanan; U.A.J.Pinidiyaarachchi },
publisher = {IEEE SigPort},
title = {Gate connected Convolutional Neural Network for Object Tracking},
year = {2017} }
TY - EJOUR
T1 - Gate connected Convolutional Neural Network for Object Tracking
AU - T.Kokul; C.Fookes; S.Sridharan; A.Ramanan; U.A.J.Pinidiyaarachchi
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1918
ER -
T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi. (2017). Gate connected Convolutional Neural Network for Object Tracking. IEEE SigPort. http://sigport.org/1918
T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi, 2017. Gate connected Convolutional Neural Network for Object Tracking. Available at: http://sigport.org/1918.
T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi. (2017). "Gate connected Convolutional Neural Network for Object Tracking." Web.
1. T.Kokul, C.Fookes, S.Sridharan, A.Ramanan, U.A.J.Pinidiyaarachchi. Gate connected Convolutional Neural Network for Object Tracking [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1918

Spatial-Sequential-Spectral Context Awareness Tracking


Visual context has formed a robust stimulation for visual perception. Spatio-temporal context in existing trackers sometimes shows weak reliability in visible light videos with poor quality. Supplemented by the infrared perception, this work exploits the role of visual context in tracking in a spatial-sequential-spectral view, by which to excavate dominance of different contexts in various scenarios.

Paper Details

Authors:
Jianwu Fang, Zheng Li, and Jianru Xue
Submitted On:
4 September 2017 - 1:19am
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Poster of paper with ID of 1919

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[1] Jianwu Fang, Zheng Li, and Jianru Xue, "Spatial-Sequential-Spectral Context Awareness Tracking", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1826. Accessed: Sep. 15, 2019.
@article{1826-17,
url = {http://sigport.org/1826},
author = {Jianwu Fang; Zheng Li; and Jianru Xue },
publisher = {IEEE SigPort},
title = {Spatial-Sequential-Spectral Context Awareness Tracking},
year = {2017} }
TY - EJOUR
T1 - Spatial-Sequential-Spectral Context Awareness Tracking
AU - Jianwu Fang; Zheng Li; and Jianru Xue
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1826
ER -
Jianwu Fang, Zheng Li, and Jianru Xue. (2017). Spatial-Sequential-Spectral Context Awareness Tracking. IEEE SigPort. http://sigport.org/1826
Jianwu Fang, Zheng Li, and Jianru Xue, 2017. Spatial-Sequential-Spectral Context Awareness Tracking. Available at: http://sigport.org/1826.
Jianwu Fang, Zheng Li, and Jianru Xue. (2017). "Spatial-Sequential-Spectral Context Awareness Tracking." Web.
1. Jianwu Fang, Zheng Li, and Jianru Xue. Spatial-Sequential-Spectral Context Awareness Tracking [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1826