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Internet of Things (IoT) Based Fire Alert Monitoring System for Car Parking

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Internet of Things (IoT) Based Fire Alert Monitoring System ...

International Conf on Electrical and Electronic Engineering (ICEEE)

作者： Anis Farihan Mat Raffei Nur Syafiqah Awang Nur Shamsiah Abdul Rahman Nor Saradatul Akmar Zulkifli Multimedia Computing & Computer Vision Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Kuantan Pahang Malaysia Information Systems Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Kuantan Pahang Malaysia Soft Computing & Intelligent System Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang Kuantan Pahang Malaysia

ISBN: (数字)9781728167886

ISBN: (纸本)9781728167893

Safety is one of the important factors that should be considered either in the parking area, workplace, home area and so forth. In the university parking area, the students are unable to receive any information regarding a fire smoke or an accident near their vehicle. In addition, the parking safety also not assured due to the shortage of car superintendence and there is no any strict parking management by the security officer. Therefore, a fire smoke alert monitoring system in the university parking area is necessary in order to prevent any accidents that may cause property breakdown and loss of life as happens inside the university area. This system should be introduced since the existing parking is unsystematic and less efficient as it unable to response the complications that are regularly happen to the students because they do not receive any information regarding a fire smoke or an accident near their vehicle in the parking area. With this new system, a few advancements are implemented in order to help the students in various aspects by using multiple and distinct Arduino devices. Moreover, an android application is developed to facilitate the security officer in order to identify the car information that are involved in the accident that might be occur in the university parking area.

关键词： Fires Monitoring Sensitivity Automobiles Sensors Accidents Internet of Things

Image Captioning with Clause-Focused Metrics in a Multi-Modal Setting for Marketing

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arXiv 2019年

作者： Harzig, Philipp Zecha, Dan Lienhart, Rainer Kaiser, Carolin Schallner, René Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg86159 Germany GfK Verein Nuremberg90419 Germany

Automatically generating descriptive captions for images is a well-researched area in computer vision. However, existing evaluation approaches focus on measuring the similarity between two sentences disregarding fine-grained semantics of the captions. In our setting of images depicting persons interacting with branded products, the subject, predicate, object and the name of the branded product are important evaluation criteria of the generated captions. Generating image captions with these constraints is a new challenge, which we tackle in this work. By simultaneously predicting integer-valued ratings that describe attributes of the human-product interaction, we optimize a deep neural network architecture in a multi-task learning setting, which considerably improves the caption quality. Furthermore, we introduce a novel metric that allows us to assess whether the generated captions meet our requirements (i.e., subject, predicate, object, and product name) and describe a series of experiments on caption quality and how to address annotator disagreements for the image ratings with an approach called soft targets. We also show that our novel clause-focused metrics are also applicable to other image captioning datasets, such as the popular MSCOCO dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Integer programming

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

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arXiv 2024年

作者： Dai, Yuekun Zhang, Dafeng Li, Xiaoming Yue, Zongsheng Li, Chongyi Zhou, Shangchen Feng, Ruicheng Yang, Peiqing Jin, Zhezhu Liu, Guanqun Loy, Chen Change Zhang, Lize Liu, Shuai Feng, Chaoyu Wang, Luyang Chen, Shuan Shao, Guangqi Wang, Xiaotao Lei, Lei Yang, Qirui Cheng, Qihua Xu, Zhiqiang Liu, Yihao Yue, Huanjing Yang, Jingyu Vasluianu, Florin-Alexandru Wu, Zongwei Ciubotariu, George Timofte, Radu Zhang, Zhao Zhao, Suiyi Wang, Bo Zuo, Zhichao Wei, Yanyan Teja, Kuppa Sai Sri Jayakar Reddy, A. Rongali, Girish Mitra, Kaushik Ma, Zhihao Liu, Yongxu Zhang, Wanying Shang, Wei He, Yuhong Peng, Long Yu, Zhongxin Luo, Shaofei Wang, Jian Miao, Yuqi Li, Baiang Wei, Gang Verma, Rakshank Maheshwari, Ritik Tekchandani, Rahul Hambarde, Praful Tazi, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Zhang, Haopeng Hou, Yingli Yao, Mingde Levin, M.S. Sundararajan, Aniruth Hari Kumar, A. Xiaomi Inc. China School of Electrical and Information Engineering Tianjin University China Shenzhen MicroBT Electronics Technology Co. Ltd China Shanghai Artificial Intelligence Laboratory China Computer Vision Lab CAIDAS IFI University of Würzburg Germany Laboratory for Multimedia Computing Hefei University of Technology China Detect Technologies Pvt Ltd India Indian Institute of Technology Madras India Xidian University China Harbin Institute of Technology China Northeastern University China University of Science and Technology of China China Fujian Normal University China Snap Inc. United States Tongji University China Hefei University of Technology China GEC Ajmer India CVPR Lab IIT Ropar India SCSS Trinity College Dublin Ireland Faculty of Robot Science and Engineering Northeastern University China Software College Northeastern University China The Chinese University of Hong Kong China Shiv Nadar University India

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and CVPR 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024. Copyright © 2024, The Authors. All rights reserved.

关键词： Image sensors

Template Matching Analysis using Neural Network for Mobile Iris Recognition System

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IOP Conference Series: Materials Science and Engineering 2020年第1期769卷

作者： Anis Farihan Mat Raffei Siti Zulaikha Dzulkifli Nur Shamsiah Abdul Rahman Multimedia & Computer Vision Research group Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Malaysia

Today, the use of mobile phone among individuals with an advanced technology, acquisition and computation resources is crucial in securing personal data and services as well as in identifying a person. Furthermore, there has been growing interest of researchers using mobile as an acquisition device to capture an eye image in a non-cooperative environment (static motion and at different distances) for iris recognition. However, the use of mobile as an acquisition device still facing some noise factors because the quality of image captured in this environmental condition is low compared to a camera device. Besides, low awareness of user in handling the mobile devices and lack technical experiences in capturing iris images with this environmental condition is generally uncontrolled contribute to producing unpredictable low quality of eye images. Due to these issues, it led to incorrect segmentation of iris boundaries and subsequently lowered the ability to match the iris features. Hence, this condition contributes to performance degradation of iris recognition system. Therefore, to improve the ability to match the iris features in mobile iris recognition system, a combination with neural network is proposed. The iris database used to test against these methods is CASIA-Iris-M1-S2. The proposed method, a combination of Hamming distance and neural network has achieved a better result where it increases the existing method in term of accuracy for mobile iris recognition under non-cooperative environment.

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Addressing data bias problems for chest X-ray image report generation

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arXiv 2019年

作者： Harzig, Philipp Chen, Yan-Ying Chen, Francine Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg Germany FX Palo Alto Laboratory 3174 Porter Drive Palo AltoCA United States

Automatic medical report generation from chest X-ray images is one possibility for assisting doctors to reduce their workload. However, the different patterns and data distribution of normal and abnormal cases can bias machine learning models. Previous attempts did not focus on isolating the generation of the abnormal and normal sentences in order to increase the variability of generated paragraphs. To address this, we propose to separate abnormal and normal sentence generation by using a dual word LSTM in a hierarchical LSTM model. In addition, we conduct an analysis on the distinctiveness of generated sentences compared to the BLEU score, which increases when less distinct reports are generated. Together with this analysis, we propose a way of selecting a model that generates more distinctive sentences. We hope our findings will help to encourage the development of new metrics to better verify methods of automatic medical report generation. Copyright © 2019, The Authors. All rights reserved.

关键词： Long short-term memory

Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming

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arXiv 2018年

作者： Einfalt, Moritz Zecha, Dan Lienhart, Rainer Multimedia Computing and Computer Vision Lab University of Augsburg Germany

In this paper we consider the problem of human pose estimation in real-world videos of swimmers. Swimming channels allow filming swimmers simultaneously above and below the water surface with a single stationary camera. These recordings can be used to quantitatively assess the athletes’ performance. The quantitative evaluation, so far, requires manual annotations of body parts in each video frame. We therefore apply the concept of CNNs in order to automatically infer the required pose information. Starting with an off-the-shelf architecture, we develop extensions to leverage activity information – in our case the swimming style of an athlete – and the continuous nature of the video recordings. Our main contributions are threefold: (a) We apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a baseline in our very challenging aquatic environment and discuss its error modes, (b) we propose an extension to input swimming style information into the fully convolutional architecture and (c) modify the architecture for continuous pose estimation in videos. With these additions we achieve reliable pose estimates with up to +16% more correct body joint detections compared to the baseline architecture. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

Pose estimation for deriving kinematic parameters of competitive swimmers

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Pose estimation for deriving kinematic parameters of competi...

computer vision Applications in Sports 2017

作者： Zecha, Dan Eggert, Christian Lienhart, Rainer Multimedia Computing and Computer Vision Lab. Augsburg University Germany

In the field of competitive swimming a quantitative evaluation of kinematic parameters is a valuable tool for coaches but also a labor intensive task. We present a system which is able to automate the extraction of many kinematic parameters such as stroke frequency, kick rates and stroke-specific intra-cyclic parameters from video footage of an athlete. While this task can in principle be solved by human pose estimation, the problem is exacerbated by permanently changing self-occlusion and severe noise caused by air bubbles, splashes, light reflection and light refraction. Current approaches for pose estimation are unable to provide the necessary localization precision under these conditions in order to enable accurate estimates of all desired kinematic parameters. In this paper we reduce the problem of kinematic parameter derivation to detecting key frames with a deep neural network human pose estimator. We show that we can correctly detect key frames with a precision which is on par with the human annotation performance. From the correctly located key frames, aforementioned parameters can be successfully inferred. © 2017, Society for Imaging Science and Technology.

关键词： Deep neural networks

Multimodal image captioning for marketing analysis

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arXiv 2018年

作者： Harzig, Philipp Brehm, Stephan Lienhart, Rainer Kaiser, Carolin Schallner, René Multimedia Computing and Computer Vision Lab University of Augsburg Augsburg86159 Germany GfK Verein Nuremberg90419 Germany

Automatically captioning images with natural language sentences is an important research topic. State of the art models are able to produce human-like sentences. These models typically describe the depicted scene as a whole and do not target specific objects of interest or emotional relationships between these objects in the image. However, marketing companies require to describe these important attributes of a given scene. In our case, objects of interest are consumer goods, which are usually identifiable by a product logo and are associated with certain brands. From a marketing point of view, it is desirable to also evaluate the emotional context of a trademarked product, i.e., whether it appears in a positive or a negative *** address the problem of finding brands in images and deriving corresponding captions by introducing a modified image captioning network. We also add a third output modality, which simultaneously produces real-valued image ratings. Our network is trained using a classificationaware loss function in order to stimulate the generation of sentences with an emphasis on words identifying the brand of a product. We evaluate our model on a dataset of images depicting interactions between humans and branded products. The introduced network improves mean class accuracy by 24.5 percent. Thanks to adding the third output modality, it also considerably improves the quality of generated captions for images depicting branded products. Copyright © 2018, The Authors. All rights reserved.

关键词： Image enhancement

The First Visual Object Tracking Segmentation VOTS2023 Challenge Results

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The First Visual Object Tracking Segmentation VOTS2023 Chall...

International Conference on computer vision Workshops (ICCV Workshops)

作者： Matej Kristan Jiří Matas Martin Danelljan Michael Felsberg Hyung Jin Chang Luka Čehovin Zajc Alan Lukežič Ondrej Drbohlav Zhongqun Zhang Khanh-Tung Tran Xuan-Son Vu Johanna Björklund Christoph Mayer Yushan Zhang Lei Ke Jie Zhao Gustavo Fernández Noor Al-Shakarji Dong An Michael Arens Stefan Becker Goutam Bhat Sebastian Bullinger Antoni B. Chan Shijie Chang Hanyuan Chen Xin Chen Yan Chen Zhenyu Chen Yangming Cheng Yutao Cui Chunyuan Deng Jiahua Dong Matteo Dunnhofer Wei Feng Jianlong Fu Jie Gao Ruize Han Zeqi Hao Jun-Yan He Keji He Zhenyu He Xiantao Hu Kaer Huang Yuqing Huang Yi Jiang Ben Kang Jin-Peng Lan Hyungjun Lee Chenyang Li Jiahao Li Ning Li Wangkai Li Xiaodi Li Xin Li Pengyu Liu Yue Liu Huchuan Lu Bin Luo Ping Luo Yinchao Ma Deshui Miao Christian Micheloni Kannappan Palaniappan Hancheol Park Matthieu Paul HouWen Peng Zekun Qian Gani Rahmon Norbert Scherer-Negenborn Pengcheng Shao Wooksu Shin Elham Soltani Kazemi Tianhui Song Rainer Stiefelhagen Rui Sun Chuanming Tang Zhangyong Tang Imad Eddine Toubal Jack Valmadre Joost van de Weijer Luc Van Gool Jash Vira Stèphane Vujasinović Cheng Wan Jia Wan Dong Wang Fei Wang Feifan Wang He Wang Limin Wang Song Wang Yaowei Wang Zhepeng Wang Gangshan Wu Jiannan Wu Qiangqiang Wu Xiaojun Wu Anqi Xiao Jinxia Xie Chenlong Xu Min Xu Tianyang Xu Yuanyou Xu Bin Yan Dawei Yang Ming-Hsuan Yang Tianyu Yang Yi Yang Zongxin Yang Xuanwu Yin Fisher Yu Hongyuan Yu Qianjin Yu Weichen Yu YongSheng Yuan Zehuan Yuan Jianlin Zhang Lu Zhang Tianzhu Zhang Guodongfang Zhao Shaochuan Zhao Yaozong Zheng Bineng Zhong Jiawen Zhu Xuefeng Zhu Yueting Zhuang ChengAo Zong Kunlong Zuo University of Ljubljana Slovenia Czech Technical University Czech Republic ETH Zürich Switzerland Linköping University Sweden University of Birmingham United Kingdom Umeå University Sweden Dalian University of Technology China Austrian Institute of Technology Austria University of Missouri USA Institute of Automation China Fraunhofer IOSB Germany City University of Hong Kong Hong Kong China DAMO Academy China HuaQiao University China Zhejiang University China Nanjing University China Georgia Institute of Technology USA Shenyang Institute of Automation Chinese Academia of Science China University of Udine Italy Tianjin University China Microsoft Research Asia China Harbin Institute of Technology (Shenzhen) China Guangxi Normal University China Lenovo Research China ByteDance China Nota AI South Korea University of Science and Technology of China China Peng Cheng Laboratory China Jiangnan University China The University of Hong Kong Hong Kong China Microsoft Research China Karlsruhe Institute of Technology (KIT) Germany University of Chinese Academy of Sciences China University of Adelaide Australia Computer Vision Center Spain Boston College USA University of South Carolina USA Carnegie Mellon University USA University of California at Merced USA International Digital Economy Academy China Multimedia Department Xiaomi Inc China Institute of Computing Technology Chinese Academy of Sciences China

The Visual Object Tracking Segmentation VOTS2023 challenge is the eleventh annual tracker benchmarking activity of the VOT initiative. This challenge is the first to merge short-term and long-term as well as single-target and multiple-target tracking with segmentation masks as the only target location specification. A new dataset was created; the ground truth has been withheld to prevent overfitting. New performance measures and evaluation protocols have been created along with a new toolkit and an evaluation server. Results of the presented 47 trackers indicate that modern tracking frameworks are well-suited to deal with convergence of short-term and long-term tracking and that multiple and single target tracking can be considered a single problem. A leaderboard, with participating trackers details, the source code, the datasets, and the evaluation kit are publicly available at the challenge website 1 .

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