short-paper

Real-time target detection and recognition with deep convolutional networks for intelligent visual surveillance

Authors:

Jie CaoAuthors Info & Claims

UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing

Pages 321 - 326

https://doi.org/10.1145/2996890.3007881

Published: 06 December 2016 Publication History

Abstract

Moving target detection and tracking, recognition, behaviors analysis are the key issues in the intelligent visual surveillance system (IVSS). The challenge is how to process the real-time video stream in an effective way in case that we could find the interested objects for analysis. However, the traditional video surveillance technology often does not meet the needs of real-time key frame recognition for the on-line intelligent video monitoring system. In our paper, we adopt the state-of-the-art Faster R-CNN [7] that takes advantages of convolutional neural networks into our real-time target recognition system - Deep Intelligent Visual Surveillance (DIVS). The key aspects of our DIVS are consisted of four parts: (i) Getting the real-time video images from remote cameras; (ii) Processing the data with the deep learning framework caffe [23] built for Faster R-CNN; (iii) Storing the valuable data with MySQL; (iv) Data presentation on the website. Experiments based on our system validated the effectiveness, stability and accuracy of our proposed solutions.

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Cited By

Yadav RGupta AFulara VVerma MYadav VRawat R(2024)Intelligent Surveillance System Using Deep LearningProceedings of Data Analytics and Management10.1007/978-981-99-6547-2_31(405-416)Online publication date: 3-Jan-2024
https://doi.org/10.1007/978-981-99-6547-2_31
Singhal PWalambe RRamanna SKotecha K(2023)Domain Adaptation: Challenges, Methods, Datasets, and ApplicationsIEEE Access10.1109/ACCESS.2023.323702511(6973-7020)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3237025
Fang HZhang ZDi DZhang JSun BWang NLi B(2023)Integrating fluid–solid coupling domain knowledge with deep learning models: An automatic and interpretable diagnostic system for the silting disease of drainage pipelinesTunnelling and Underground Space Technology10.1016/j.tust.2023.105386142(105386)Online publication date: Dec-2023
https://doi.org/10.1016/j.tust.2023.105386
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cover image ACM Other conferences

UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing

December 2016

549 pages

ISBN:9781450346160

DOI:10.1145/2996890

General Chairs:
Changjun Jiang
Tongji University, China
,
Omer Rana
Cardiff University, UK
,
Nick Antonopoulos
University of Derby, UK

Copyright © 2016 ACM.

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]

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Publication History

Published: 06 December 2016

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UCC '16

UCC '16: 9th International Conference on Utility and Cloud Computing

December 6 - 9, 2016

Shanghai, China

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Cited By

Yadav RGupta AFulara VVerma MYadav VRawat R(2024)Intelligent Surveillance System Using Deep LearningProceedings of Data Analytics and Management10.1007/978-981-99-6547-2_31(405-416)Online publication date: 3-Jan-2024
https://doi.org/10.1007/978-981-99-6547-2_31
Singhal PWalambe RRamanna SKotecha K(2023)Domain Adaptation: Challenges, Methods, Datasets, and ApplicationsIEEE Access10.1109/ACCESS.2023.323702511(6973-7020)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3237025
Fang HZhang ZDi DZhang JSun BWang NLi B(2023)Integrating fluid–solid coupling domain knowledge with deep learning models: An automatic and interpretable diagnostic system for the silting disease of drainage pipelinesTunnelling and Underground Space Technology10.1016/j.tust.2023.105386142(105386)Online publication date: Dec-2023
https://doi.org/10.1016/j.tust.2023.105386
Xu WHe JShu Y(2020)Transfer Learning and Deep Domain AdaptationAdvances and Applications in Deep Learning [Working Title]10.5772/intechopen.94072Online publication date: 29-Oct-2020
https://doi.org/10.5772/intechopen.94072
Suarez-Paez JSalcedo-Gonzalez MClimente AEsteve MGómez JPalau CPérez-Llopis I(2019)A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security CentersInformation10.3390/info1012036510:12(365)Online publication date: 24-Nov-2019
https://doi.org/10.3390/info10120365
Xie DXu YWang R(2019)Obstacle detection and tracking method for autonomous vehicle based on three-dimensional LiDARInternational Journal of Advanced Robotic Systems10.1177/172988141983158716:2Online publication date: 20-Mar-2019
https://doi.org/10.1177/1729881419831587
Zhang CLiu SHe J(2018)The Study of Surface State Identification Based on BP_adaboost Algorithm2018 37th Chinese Control Conference (CCC)10.23919/ChiCC.2018.8483380(5865-5870)Online publication date: Jul-2018
https://doi.org/10.23919/ChiCC.2018.8483380
Choi SSalter EZhang XWunsche B(2018)Bird Eyes: A Cloud-Based Object Detection System for Customisable Surveillance2018 International Conference on Image and Vision Computing New Zealand (IVCNZ)10.1109/IVCNZ.2018.8634751(1-6)Online publication date: Nov-2018
https://doi.org/10.1109/IVCNZ.2018.8634751

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