Scene Recognition and Object Detection in a Unified Convolutional Neural Network on a Mobile Manipulator

Sun, Hao; Meng, Zehui; Tao, Pey Yuen; Ang, Marcelo H.

doi:10.1109/icra.2018.8460535

Cited by 20 publications

(14 citation statements)

References 26 publications

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“…In the literature, depending on the final goal pursued, the approaches emphasize some sub-tasks more than others. In most of the reviewed works, the scene understanding is oriented to object detection [89,107,176,206], scene recognition [54,55,158,160], 3D reconstruction [91,147,157,199,200] and semantic segmentation [7,176,201,205]. Other approaches combine some of these sub-tasks such as [54,158].…”

Section: Related Workmentioning

confidence: 99%

“…In most of the reviewed works, the scene understanding is oriented to object detection [89,107,176,206], scene recognition [54,55,158,160], 3D reconstruction [91,147,157,199,200] and semantic segmentation [7,176,201,205]. Other approaches combine some of these sub-tasks such as [54,158]. Furthermore, they may be aimed at unknown, partially known and completely known environments.…”

Section: Related Workmentioning

confidence: 99%

“…The output of the system is a map that uses the results of the scene labels to create a semantic map of the environment. The work by Sun et al [158] addresses the scene recognition and object detection tasks jointly. A map of the environment shown in Fig.…”

Section: Scene Understanding Considering the Whole Environmentmentioning

confidence: 99%

See 2 more Smart Citations

Adding Uncertainty to an Object Detection System for Mobile Robots

Hernandez

Gómez

Crespo

et al. 2017

2017 6th International Conference on Space Mission Challenges for Information Technology (SMC-IT)

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I would like to dedicate these lines to express my gratitude to all the people who have helped me in a way or another to achieve this goal that is so important to me. First, I would like to thank my tutor and director, Dr. Ramón Barber, for introducing me to the exciting world of research, for all his support, guidance, trust, and patience over these years of my Ph.D. studies. Thank you for giving me the opportunity to work with you. I also thank Dr. Oscar Martínez, for all the advice and technical talks and for allowing me to visit his laboratory in Sweden. I would like to thank the University Carlos III of Madrid and all the Robotics Lab members I met during these years. Thank you for your support, professionalism, and friendship; It is an honor to belong to this great research group.I would like to especially thank my lab partner Clara Gómez. Thank you for being such a great partner and friend over these years, for your support, professionalism, your dedication at work. Thank you for being my partner in conferences and research visits. Definitely, the Ph.D. would not have been the same without you.I would like to thank Dr. Zoltan Marton and his team for giving me the opportunity to work with them at the DLR institute in Germany. I would also like to thank Prof. Robert Babuška for welcoming me to his lab at the CTU in Prague. Especially thanks to Erik Derner for being a great lab partner, for our interesting discussions, for his professionalism and friendship. I also thank the people I met at Örebro University, especially Eduardo Gutiérrez; thank you for your support and friendship.Finally and most importantly, I would like to thank my family. Thank you for all your support, your love, and for always being there. Despite the more than 7000 km. that separate us, I am always with you, and I know that you are with me celebrating this achievement. I leave for last, the person I must thank for having embarked on this path. I want to thank my husband, Jesús. Thank you for supporting me and helping me accomplish this goal, for being my partner on this roller coaster of life, for always believing in me, and for having a word of encouragement when I felt like I could not continue. Without you, I am sure I would not have made it. For you all my love and my gratitude.To all my sincere thanks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Adding Uncertainty to an Object Detection System for Mobile Robots

Hernandez

Gómez

Crespo

et al. 2017

2017 6th International Conference on Space Mission Challenges for Information Technology (SMC-IT)

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“…To this end, in the paper we propose a novel 1-dimensional Convolutional Neural Network (C1D) including two branches, one for activity recognition and the other for indoor localization. To date, conventional 2-dimensional Convolutional Neural Networks (C2D), which have brilliant ability to learn features from raw data, boost the development of computer vision [24]- [29], robotics [30]- [33], machinery [34]- [36], etc. Unlike C2D that processes 2D spatial data such as images, C1D is capable to process 1D temporal data.…”

Section: Introductionmentioning

confidence: 99%

Joint Activity Recognition and Indoor Localization With WiFi Fingerprints

et al. 2019

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Recent years have witnessed the rapid development in the research topic of WiFi sensing that automatically senses human with commercial WiFi devices. Past work falls into two major categories, i.e., activity recognition and the indoor localization. The former work utilizes WiFi devices to recognize human daily activities such as smoking, walking, and dancing. The latter one, indoor localization, can be used for indoor navigation, location-based services, and through-wall surveillance. The key rationale behind WiFi sensing is that people behaviors can influence the WiFi signal propagation and introduce specific patterns into WiFi signals, called WiFi fingerprints, which can be further explored to identify human activities and locations. In this paper, we propose a novel deep learning framework for joint activity recognition and indoor localization task using WiFi channel state information (CSI) fingerprints. More precisely, we develop a system running standard IEEE 802.11n WiFi protocol and collect more than 1400 CSI fingerprints on 6 activities at 16 indoor locations. Then we propose a dual-task convolutional neural network with one-dimensional convolutional layers for the joint task of activity recognition and indoor localization. The experimental results and ablation study show that our approach achieves good performances in this joint WiFi sensing task. Data and code have been made publicly available at https://github.com/geekfeiw/apl.

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“…Scene recognition can be widely applied in many realworld problems. One such example is in robotics [2], [3]. It is crucial for understanding the environment [1].…”

Section: Introductionmentioning

confidence: 99%

An Efficient RGB-D Scene Recognition Method Based on Multi-Information Fusion

Gong

Zhang

2020

IEEE Access

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RGB-D scene recognition is a challenging problem due to lack of RGB-D datasets and inefficient RGB-D fusion. In this paper, we exploit several factors that affect the RGB-D scene recognition performance, including the representations of the RGB-D data and objects in the scene. We propose an effective multi-information fusion method composed of two modules: a revised detection-based method, and a multi-feature fusion based classifier. The revised detection-based method leverage the auxiliary RGB data. And the multi-feature fusion based classifier select the optimal feature configuration for RGB-D data description. The proposed method is validated on two publicly available datasets: the SUN RGB-D dataset, and the NYU Depth v2 dataset. The obtained results show that the proposed fusion method is effective and is comparable with the state-of-the-art method. Furthermore, the proposed framework contains much less parameters than the state-of-the-art model and thus requires much less time for training. The code and the fine-tuned model parameters are available at: https://github.com/zhangbin28/MulInfo_RGBDScene.

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Scene Recognition and Object Detection in a Unified Convolutional Neural Network on a Mobile Manipulator

Cited by 20 publications

References 26 publications

Adding Uncertainty to an Object Detection System for Mobile Robots

Adding Uncertainty to an Object Detection System for Mobile Robots

Joint Activity Recognition and Indoor Localization With WiFi Fingerprints

An Efficient RGB-D Scene Recognition Method Based on Multi-Information Fusion

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