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SIDAS‘17

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Authors:
Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen
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11 October 2017 - 2:41pm
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SIDAS2017_materials.pdf

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[1] Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen, "SIDAS‘17", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2257. Accessed: Sep. 24, 2020.
@article{2257-17,
url = {http://sigport.org/2257},
author = {Changshui Zhang; Shefeng Yan; Liwei Wang; Shiguang Shan; Jingdong Chen; Fangjiong Chen },
publisher = {IEEE SigPort},
title = {SIDAS‘17},
year = {2017} }
TY - EJOUR
T1 - SIDAS‘17
AU - Changshui Zhang; Shefeng Yan; Liwei Wang; Shiguang Shan; Jingdong Chen; Fangjiong Chen
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2257
ER -
Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen. (2017). SIDAS‘17. IEEE SigPort. http://sigport.org/2257
Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen, 2017. SIDAS‘17. Available at: http://sigport.org/2257.
Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen. (2017). "SIDAS‘17." Web.
1. Changshui Zhang, Shefeng Yan, Liwei Wang, Shiguang Shan, Jingdong Chen, Fangjiong Chen. SIDAS‘17 [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2257

Adversarial Networks for Secure Wireless Communications - Slides


We propose a data-driven secure wireless communication scheme, in which the goal is to transmit a signal to a legitimate receiver with minimal distortion, while keeping some information about the signal private from an eavesdropping adversary. When the data distribution is known, the optimal trade-off between the reconstruction quality at the legitimate receiver and the leakage to the adversary can be characterised in the information theoretic asymptotic limit.

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Authors:
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz
Submitted On:
16 May 2020 - 4:33pm
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[1] Thomas Marchioro, Nicola Laurenti, Deniz Gunduz, "Adversarial Networks for Secure Wireless Communications - Slides", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5380. Accessed: Sep. 24, 2020.
@article{5380-20,
url = {http://sigport.org/5380},
author = {Thomas Marchioro; Nicola Laurenti; Deniz Gunduz },
publisher = {IEEE SigPort},
title = {Adversarial Networks for Secure Wireless Communications - Slides},
year = {2020} }
TY - EJOUR
T1 - Adversarial Networks for Secure Wireless Communications - Slides
AU - Thomas Marchioro; Nicola Laurenti; Deniz Gunduz
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5380
ER -
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. (2020). Adversarial Networks for Secure Wireless Communications - Slides. IEEE SigPort. http://sigport.org/5380
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz, 2020. Adversarial Networks for Secure Wireless Communications - Slides. Available at: http://sigport.org/5380.
Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. (2020). "Adversarial Networks for Secure Wireless Communications - Slides." Web.
1. Thomas Marchioro, Nicola Laurenti, Deniz Gunduz. Adversarial Networks for Secure Wireless Communications - Slides [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5380

A Single-Wavelength Real-Time Material-Sensing Camera Based on Time-of-Flight Measurements


Time-of-Flight (ToF) cameras provide a fast and robust way of acquiring the 3D shape of real scenes. Dense depth images can be generated at tens of frame per second. 3D shapes can be then segmented and objects classified, but can we directly sense the objects’ material using just a ToF camera? This live demonstration proves the answer to be affirmative. This possibility has only very recently been unveiled and we are, to the best of our knowledge, the first providing a live demonstrator showing the feasibility of this approach.

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Authors:
Otmar Loffeld, Thomas Kerstein, Bernd Buxbaum
Submitted On:
14 May 2020 - 3:55am
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heredia_ICASSP_2020_poster.pdf

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[1] Otmar Loffeld, Thomas Kerstein, Bernd Buxbaum, "A Single-Wavelength Real-Time Material-Sensing Camera Based on Time-of-Flight Measurements", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5259. Accessed: Sep. 24, 2020.
@article{5259-20,
url = {http://sigport.org/5259},
author = {Otmar Loffeld; Thomas Kerstein; Bernd Buxbaum },
publisher = {IEEE SigPort},
title = {A Single-Wavelength Real-Time Material-Sensing Camera Based on Time-of-Flight Measurements},
year = {2020} }
TY - EJOUR
T1 - A Single-Wavelength Real-Time Material-Sensing Camera Based on Time-of-Flight Measurements
AU - Otmar Loffeld; Thomas Kerstein; Bernd Buxbaum
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5259
ER -
Otmar Loffeld, Thomas Kerstein, Bernd Buxbaum. (2020). A Single-Wavelength Real-Time Material-Sensing Camera Based on Time-of-Flight Measurements. IEEE SigPort. http://sigport.org/5259
Otmar Loffeld, Thomas Kerstein, Bernd Buxbaum, 2020. A Single-Wavelength Real-Time Material-Sensing Camera Based on Time-of-Flight Measurements. Available at: http://sigport.org/5259.
Otmar Loffeld, Thomas Kerstein, Bernd Buxbaum. (2020). "A Single-Wavelength Real-Time Material-Sensing Camera Based on Time-of-Flight Measurements." Web.
1. Otmar Loffeld, Thomas Kerstein, Bernd Buxbaum. A Single-Wavelength Real-Time Material-Sensing Camera Based on Time-of-Flight Measurements [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5259

Test

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Authors:
Submitted On:
27 April 2020 - 11:14am
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Test

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[1] , "Test", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5113. Accessed: Sep. 24, 2020.
@article{5113-20,
url = {http://sigport.org/5113},
author = { },
publisher = {IEEE SigPort},
title = {Test},
year = {2020} }
TY - EJOUR
T1 - Test
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5113
ER -
. (2020). Test. IEEE SigPort. http://sigport.org/5113
, 2020. Test. Available at: http://sigport.org/5113.
. (2020). "Test." Web.
1. . Test [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5113

Semantrix: A Compressed Semantic Matrix


We present a compact data structure to represent both the duration and length of homogeneous segments of trajectories from moving objects in a way that, as a data warehouse, it allows us to efficiently answer cumulative queries. The division of trajectories into relevant segments has been studied in the literature under the topic of Trajectory Segmentation. In this paper, we design a data structure to compactly represent them and the algorithms to answer the more relevant queries.

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Authors:
Nieves R. Brisaboa, Antonio Fariña, Gonzalo Navarro
Submitted On:
20 March 2020 - 3:35pm
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DCC20.pdf

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[1] Nieves R. Brisaboa, Antonio Fariña, Gonzalo Navarro, "Semantrix: A Compressed Semantic Matrix", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5011. Accessed: Sep. 24, 2020.
@article{5011-20,
url = {http://sigport.org/5011},
author = {Nieves R. Brisaboa; Antonio Fariña; Gonzalo Navarro },
publisher = {IEEE SigPort},
title = {Semantrix: A Compressed Semantic Matrix},
year = {2020} }
TY - EJOUR
T1 - Semantrix: A Compressed Semantic Matrix
AU - Nieves R. Brisaboa; Antonio Fariña; Gonzalo Navarro
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5011
ER -
Nieves R. Brisaboa, Antonio Fariña, Gonzalo Navarro. (2020). Semantrix: A Compressed Semantic Matrix. IEEE SigPort. http://sigport.org/5011
Nieves R. Brisaboa, Antonio Fariña, Gonzalo Navarro, 2020. Semantrix: A Compressed Semantic Matrix. Available at: http://sigport.org/5011.
Nieves R. Brisaboa, Antonio Fariña, Gonzalo Navarro. (2020). "Semantrix: A Compressed Semantic Matrix." Web.
1. Nieves R. Brisaboa, Antonio Fariña, Gonzalo Navarro. Semantrix: A Compressed Semantic Matrix [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5011

Graph Regularization Network with Semantic Affinity for Weakly-supervised Temporal Action Localization


This paper presents a novel deep architecture for weakly-supervised temporal action localization that predicts temporal boundaries with graph regularization. Our model not only generates segment-level action responses but also propagates segment-level responses to
neighborhood in a form of graph Laplacian regularization. Specifically, our approach consists of two sub-modules; a class activation
module to estimate the action score map over time through the action classifiers, and a graph regularization module to refine the

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Authors:
Jungin Park, Jiyoung Lee, Sangryul Jeon, Seungryong Kim, Kwanghoon Sohn
Submitted On:
20 September 2019 - 7:59pm
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Park_Oral_ICIP_2019.pdf

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[1] Jungin Park, Jiyoung Lee, Sangryul Jeon, Seungryong Kim, Kwanghoon Sohn, "Graph Regularization Network with Semantic Affinity for Weakly-supervised Temporal Action Localization", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4797. Accessed: Sep. 24, 2020.
@article{4797-19,
url = {http://sigport.org/4797},
author = {Jungin Park; Jiyoung Lee; Sangryul Jeon; Seungryong Kim; Kwanghoon Sohn },
publisher = {IEEE SigPort},
title = {Graph Regularization Network with Semantic Affinity for Weakly-supervised Temporal Action Localization},
year = {2019} }
TY - EJOUR
T1 - Graph Regularization Network with Semantic Affinity for Weakly-supervised Temporal Action Localization
AU - Jungin Park; Jiyoung Lee; Sangryul Jeon; Seungryong Kim; Kwanghoon Sohn
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4797
ER -
Jungin Park, Jiyoung Lee, Sangryul Jeon, Seungryong Kim, Kwanghoon Sohn. (2019). Graph Regularization Network with Semantic Affinity for Weakly-supervised Temporal Action Localization. IEEE SigPort. http://sigport.org/4797
Jungin Park, Jiyoung Lee, Sangryul Jeon, Seungryong Kim, Kwanghoon Sohn, 2019. Graph Regularization Network with Semantic Affinity for Weakly-supervised Temporal Action Localization. Available at: http://sigport.org/4797.
Jungin Park, Jiyoung Lee, Sangryul Jeon, Seungryong Kim, Kwanghoon Sohn. (2019). "Graph Regularization Network with Semantic Affinity for Weakly-supervised Temporal Action Localization." Web.
1. Jungin Park, Jiyoung Lee, Sangryul Jeon, Seungryong Kim, Kwanghoon Sohn. Graph Regularization Network with Semantic Affinity for Weakly-supervised Temporal Action Localization [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4797

EVALUATION OF NON-INTRUSIVE LOAD MONITORING ALGORITHMS FOR APPLIANCE-LEVEL ANOMALY DETECTION

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Authors:
Lina Stankovic, Pushpendra Singh
Submitted On:
9 May 2019 - 7:35am
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icassp2019_Presentation_Haroon.pdf

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[1] Lina Stankovic, Pushpendra Singh, "EVALUATION OF NON-INTRUSIVE LOAD MONITORING ALGORITHMS FOR APPLIANCE-LEVEL ANOMALY DETECTION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4191. Accessed: Sep. 24, 2020.
@article{4191-19,
url = {http://sigport.org/4191},
author = {Lina Stankovic; Pushpendra Singh },
publisher = {IEEE SigPort},
title = {EVALUATION OF NON-INTRUSIVE LOAD MONITORING ALGORITHMS FOR APPLIANCE-LEVEL ANOMALY DETECTION},
year = {2019} }
TY - EJOUR
T1 - EVALUATION OF NON-INTRUSIVE LOAD MONITORING ALGORITHMS FOR APPLIANCE-LEVEL ANOMALY DETECTION
AU - Lina Stankovic; Pushpendra Singh
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4191
ER -
Lina Stankovic, Pushpendra Singh. (2019). EVALUATION OF NON-INTRUSIVE LOAD MONITORING ALGORITHMS FOR APPLIANCE-LEVEL ANOMALY DETECTION. IEEE SigPort. http://sigport.org/4191
Lina Stankovic, Pushpendra Singh, 2019. EVALUATION OF NON-INTRUSIVE LOAD MONITORING ALGORITHMS FOR APPLIANCE-LEVEL ANOMALY DETECTION. Available at: http://sigport.org/4191.
Lina Stankovic, Pushpendra Singh. (2019). "EVALUATION OF NON-INTRUSIVE LOAD MONITORING ALGORITHMS FOR APPLIANCE-LEVEL ANOMALY DETECTION." Web.
1. Lina Stankovic, Pushpendra Singh. EVALUATION OF NON-INTRUSIVE LOAD MONITORING ALGORITHMS FOR APPLIANCE-LEVEL ANOMALY DETECTION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4191

Unsupervised Detection of Periodic Segments in Videos


We present a solution to the problem of discovering all periodic
segments of a video and of estimating their period in
a completely unsupervised manner. These segments may be
located anywhere in the video, may differ in duration, speed,
period and may represent unseen motion patterns of any type
of objects (e.g., humans, animals, machines, etc). The proposed
method capitalizes on earlier research on the problem
of detecting common actions in videos, also known as commonality
detection or video co-segmentation. The proposed

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Authors:
Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros
Submitted On:
5 October 2018 - 4:02am
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Unsupervised Detection of Periodic Segments in Videos

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[1] Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros, "Unsupervised Detection of Periodic Segments in Videos", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3516. Accessed: Sep. 24, 2020.
@article{3516-18,
url = {http://sigport.org/3516},
author = {Costas Panagiotakis; Giorgos Karvounas; Antonis A. Argyros },
publisher = {IEEE SigPort},
title = {Unsupervised Detection of Periodic Segments in Videos},
year = {2018} }
TY - EJOUR
T1 - Unsupervised Detection of Periodic Segments in Videos
AU - Costas Panagiotakis; Giorgos Karvounas; Antonis A. Argyros
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3516
ER -
Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros. (2018). Unsupervised Detection of Periodic Segments in Videos. IEEE SigPort. http://sigport.org/3516
Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros, 2018. Unsupervised Detection of Periodic Segments in Videos. Available at: http://sigport.org/3516.
Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros. (2018). "Unsupervised Detection of Periodic Segments in Videos." Web.
1. Costas Panagiotakis, Giorgos Karvounas, Antonis A. Argyros. Unsupervised Detection of Periodic Segments in Videos [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3516

S3D: Stacking Segmental P3D for Action Quality Assessment


Action quality assessment is crucial in areas of sports, surgery and assembly line where action skills can be evaluated. In this paper, we propose the Segment-based P3D-fused network S3D built-upon ED-TCN and push the performance on the UNLV-Dive dataset by a significant margin. We verify that segment-aware training performs better than full-video training which turns out to focus on the water spray. We show that temporal segmentation can be embedded with few efforts.

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Authors:
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran
Submitted On:
5 October 2018 - 2:08am
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AI Referee: Score Olympic Games

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[1] Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran, "S3D: Stacking Segmental P3D for Action Quality Assessment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3501. Accessed: Sep. 24, 2020.
@article{3501-18,
url = {http://sigport.org/3501},
author = {Ye Tian; Austin Reiter; Gregory D. Hager; Trac D. Tran },
publisher = {IEEE SigPort},
title = {S3D: Stacking Segmental P3D for Action Quality Assessment},
year = {2018} }
TY - EJOUR
T1 - S3D: Stacking Segmental P3D for Action Quality Assessment
AU - Ye Tian; Austin Reiter; Gregory D. Hager; Trac D. Tran
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3501
ER -
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. (2018). S3D: Stacking Segmental P3D for Action Quality Assessment. IEEE SigPort. http://sigport.org/3501
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran, 2018. S3D: Stacking Segmental P3D for Action Quality Assessment. Available at: http://sigport.org/3501.
Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. (2018). "S3D: Stacking Segmental P3D for Action Quality Assessment." Web.
1. Ye Tian, Austin Reiter, Gregory D. Hager, Trac D. Tran. S3D: Stacking Segmental P3D for Action Quality Assessment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3501

BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES

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Authors:
D. Fabrice ATREVI, Damien VIVET, Bruno EMILE
Submitted On:
20 April 2018 - 1:06am
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Poster_Icassp_ATREVI.pdf

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[1] D. Fabrice ATREVI, Damien VIVET, Bruno EMILE, "BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3071. Accessed: Sep. 24, 2020.
@article{3071-18,
url = {http://sigport.org/3071},
author = { D. Fabrice ATREVI; Damien VIVET; Bruno EMILE },
publisher = {IEEE SigPort},
title = {BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES},
year = {2018} }
TY - EJOUR
T1 - BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES
AU - D. Fabrice ATREVI; Damien VIVET; Bruno EMILE
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3071
ER -
D. Fabrice ATREVI, Damien VIVET, Bruno EMILE. (2018). BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES. IEEE SigPort. http://sigport.org/3071
D. Fabrice ATREVI, Damien VIVET, Bruno EMILE, 2018. BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES. Available at: http://sigport.org/3071.
D. Fabrice ATREVI, Damien VIVET, Bruno EMILE. (2018). "BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES." Web.
1. D. Fabrice ATREVI, Damien VIVET, Bruno EMILE. BAYESIAN GENERATIVE MODEL BASED ON COLOR HISTOGRAM OF ORIENTED PHASE AND HISTOGRAM OF ORIENTED OPTICAL FLOW FOR RARE EVENT DETECTION IN CROWDED SCENES [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3071

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