short-paper

Scene-Specific Pedestrian Detector Using Monte Carlo Framework and Faster R-CNN Deep Model: PhD Forum

Authors:

Ala Mhalla,

Thierry Chateau,

Sami Gazzah,

Najoua Essoukri Ben AmaraAuthors Info & Claims

ICDSC '16: Proceedings of the 10th International Conference on Distributed Smart Camera

Pages 228 - 229

https://doi.org/10.1145/2967413.2974040

Published: 12 September 2016 Publication History

Get Access

Abstract

In this work, we propose a novel approach to automatically specialize a generic pedestrian detector to specific scene by utilizing the sequential Monte Carlo filter and the Faster R-CNN deep model. The main idea is to consider the Faster R-CNN as a function that generates realizations from the probability distribution of the pedestrian to be detected in the target sequence. Our contribution is to approximate this target probability distribution with a set of samples and an associated specialized Faster R-CNN estimated in a sequential Bayesian filter framework. The resulting algorithm is compared to the state of the art scene specialization methods on several challenging datasets. The results are very promising.

References

[1]

X. Pan, Y. Guo, and A. Men, "Traffic surveillance system for vehicle flow detection," in ICCMS. IEEE, 2010, pp. 314--318.

Digital Library

Google Scholar

[2]

B. Benfold and I. Reid, "Stable multi-target tracking in real-time surveillance video," in CVPR. IEEE, 2011, pp. 3457--3464.

Digital Library

Google Scholar

[3]

H. Maâmatou, T. Chateau, S. Gazzah, Y. Goyat, and N. Essoukri Ben Amara, "Transductive transfer learning to specialize a generic classifier towards a specific scene," in VISAPP, 2016, pp. 411--422.

Google Scholar

[4]

X. Wang, M. Wang, and W. Li, "Scene-specific pedestrian detection for static video surveillance," PAMI, pp. 361--362, 2014.

Digital Library

Google Scholar

[5]

S. Ren, K. He, R. B. Girshick, and J. Sun, "Faster R-CNN: towards real-time object detection with region proposal networks," CoRR, 2015.

Google Scholar

[6]

M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, "The pascal visual object classes (voc) challenge," IJCV, 2010.

Digital Library

Google Scholar

[7]

M. Wang, W. Li, and X. Wang, "Transferring a generic pedestrian detector towards specific scenes," in CVPR. IEEE, 2012, pp. 3274--3281.

Digital Library

Google Scholar

[8]

Z. Y. Yunxiang Mao, "Training a scene-specific pedestrian detector using tracklets," pp. 170--176, 2015.

Digital Library

Google Scholar

Cited By

View all

Zhou LLi YRao XWang YZuo XQiao BYang Y(2022)Feature Enhancement-Based Ship Target Detection Method in Optical Remote Sensing ImagesElectronics10.3390/electronics1104063411:4(634)Online publication date: 18-Feb-2022
https://doi.org/10.3390/electronics11040634
Huang WLi GChen QJu MQu J(2021)CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target DetectionRemote Sensing10.3390/rs1305084713:5(847)Online publication date: 25-Feb-2021
https://doi.org/10.3390/rs13050847
Xu DWu Y(2020)MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target DetectionRemote Sensing10.3390/rs1219311812:19(3118)Online publication date: 23-Sep-2020
https://doi.org/10.3390/rs12193118
Show More Cited By

Recommendations

Fast R-CNN
ICCV '15: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)

This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN ...
Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images
Abstract
Pedestrian detection from a drone-based images has many potential applications such as searching for missing persons, surveillance of illegal immigrants, and monitoring of critical infrastructure. However, it is considered as a very challenge ...
Faster R-CNN: towards real-time object detection with region proposal networks
NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R-CNN [5] have reduced the running time of these detection networks, exposing region proposal computation ...

Comments

Information & Contributors

Information

Published In

ICDSC '16: Proceedings of the 10th International Conference on Distributed Smart Camera

September 2016

242 pages

ISBN:9781450347860

DOI:10.1145/2967413

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 September 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Short-paper
Research
Refereed limited

Conference

ICDSC '16

ICDSC '16: 10th international conference on distributed smart camera

September 12 - 15, 2016

Paris, France

Acceptance Rates

Overall Acceptance Rate 92 of 117 submissions, 79%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
83
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 27 Jul 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Zhou LLi YRao XWang YZuo XQiao BYang Y(2022)Feature Enhancement-Based Ship Target Detection Method in Optical Remote Sensing ImagesElectronics10.3390/electronics1104063411:4(634)Online publication date: 18-Feb-2022
https://doi.org/10.3390/electronics11040634
Huang WLi GChen QJu MQu J(2021)CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target DetectionRemote Sensing10.3390/rs1305084713:5(847)Online publication date: 25-Feb-2021
https://doi.org/10.3390/rs13050847
Xu DWu Y(2020)MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target DetectionRemote Sensing10.3390/rs1219311812:19(3118)Online publication date: 23-Sep-2020
https://doi.org/10.3390/rs12193118
Gazzah SBencharef O(2020)A Survey on how computer vision can response to urgent need to contribute in COVID-19 pandemics2020 International Conference on Intelligent Systems and Computer Vision (ISCV)10.1109/ISCV49265.2020.9204043(1-5)Online publication date: Jun-2020
https://doi.org/10.1109/ISCV49265.2020.9204043
Mhalla AChateau TGazzah SAmara N(2019)An Embedded Computer-Vision System for Multi-Object Detection in Traffic SurveillanceIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2018.287661420:11(4006-4018)Online publication date: Nov-2019
https://doi.org/10.1109/TITS.2018.2876614
Mhalla AChateau T(2019)Improving Multi Object Tracking-By-Detection Model Using a Temporal Interlaced Encoding and a Specialized Deep Detector2019 IEEE Intelligent Vehicles Symposium (IV)10.1109/IVS.2019.8814102(510-516)Online publication date: Jun-2019
https://doi.org/10.1109/IVS.2019.8814102
Gazzah SMhalla AEssoukri Ben Amara N(2016)Vehicle detection on a video traffic scene: Review and new perspectives2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)10.1109/SETIT.2016.7939912(448-454)Online publication date: Dec-2016
https://doi.org/10.1109/SETIT.2016.7939912

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Fast R-CNN

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Faster R-CNN: towards real-time object detection with region proposal networks

Comments

Published In

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Recommendations

Fast R-CNN

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Faster R-CNN: towards real-time object detection with region proposal networks

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations