Relevant bibliographies by topics / Indoor Object Detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Indoor Object Detection'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Indoor Object Detection"

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P S, Akash, and Preethi Thomas. "Indoor Object Detection for Blind." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1310–15. https://doi.org/10.21275/sr25417001126.

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Wang, Jie, Hong Zhao, Yuxi Huang, and Baotong Zhang. "Improved YOLOv8 Algorithm for Indoor Object Detection." Journal of Physics: Conference Series 3024, no. 1 (2025): 012037. https://doi.org/10.1088/1742-6596/3024/1/012037.

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Abstract To address the challenges of missed detections and false positives in indoor object detection tasks caused by target clustering and occlusion, this study proposes an advanced indoor object detection algorithm, SRSE-YOLOv8. A novel indoor object detection algorithm, SRSE-YOLOv8, is proposed to address missed and false detections caused by object clustering and occlusion in indoor scenes. First, an additional smaller detection head is introduced to enhance the capture of small objects and improve feature extraction capabilities. Then, RFAConv is incorporated to achieve input-adaptive ke
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Said, Yahia, Mohamed Atri, Marwan Ali Albahar, Ahmed Ben Atitallah, and Yazan Ahmad Alsariera. "Indoor Signs Detection for Visually Impaired People: Navigation Assistance Based on a Lightweight Anchor-Free Object Detector." International Journal of Environmental Research and Public Health 20, no. 6 (2023): 5011. http://dx.doi.org/10.3390/ijerph20065011.

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Facilitating the navigation of visually impaired people in indoor environments requires detecting indicating signs and informing them. In this paper, we proposed an indoor sign detection based on a lightweight anchor-free object detection model called FAM-centerNet. The baseline model of this work is the centerNet, which is an anchor-free object detection model with high performance and low computation complexity. A Foreground Attention Module (FAM) was introduced to extract target objects in real scenes with complex backgrounds. This module segments the foreground to extract relevant features
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Fan, Zhipeng, Wanglong Mei, Wei Liu, Ming Chen, and Zeguo Qiu. "I-DINO: High-Quality Object Detection for Indoor Scenes." Electronics 13, no. 22 (2024): 4419. http://dx.doi.org/10.3390/electronics13224419.

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Object Detection in Complex Indoor Scenes is designed to identify and categorize objects in indoor settings, with applications in areas such as smart homes, security surveillance, and home service robots. It forms the basis for advanced visual tasks including visual question answering, video description generation, and instance segmentation. Nonetheless, the task faces substantial hurdles due to background clutter, overlapping objects, and significant size differences. To tackle these challenges, this study introduces an indoor object detection approach utilizing an enhanced DINO framework. To
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Wang, Li, Ruifeng Li, Jingwen Sun, et al. "Multi-View Fusion-Based 3D Object Detection for Robot Indoor Scene Perception." Sensors 19, no. 19 (2019): 4092. http://dx.doi.org/10.3390/s19194092.

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To autonomously move and operate objects in cluttered indoor environments, a service robot requires the ability of 3D scene perception. Though 3D object detection can provide an object-level environmental description to fill this gap, a robot always encounters incomplete object observation, recurring detections of the same object, error in detection, or intersection between objects when conducting detection continuously in a cluttered room. To solve these problems, we propose a two-stage 3D object detection algorithm which is to fuse multiple views of 3D object point clouds in the first stage
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Hu, JianSheng, JunJie Ma, Bin Xiao, and Rui Zhang. "Improved Lightweight YOLOv3 model for Target Detection Algorithm." Journal of Physics: Conference Series 2370, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1742-6596/2370/1/012029.

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When detecting small objects in interior situations, the classic object detection algorithm performs poorly in terms of real-time detection task and high precision detection task. This paper suggests an optimized tiny-YOLOv3-Shufflenetv2 light-weight model based on indoor scenes. The scheme adopts the fusion light-weight network which combines ShuffleNetv2 and YOLOv3, it reduces the complexity of the model to meet the lightweight requirements while ensuring good detection results for deployment to mobile robots. Also in this paper, an indoor small target object dataset, indoor-2022, is created
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Kabir, Raihan, Yutaka Watanobe, Md Rashedul Islam, Keitaro Naruse, and Md Mostafizer Rahman. "Unknown Object Detection Using a One-Class Support Vector Machine for a Cloud–Robot System." Sensors 22, no. 4 (2022): 1352. http://dx.doi.org/10.3390/s22041352.

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Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communication and high computational power to deploy autonomous mobile robots for indoor applications. Deployment of usable indoor service robots requires uninterrupted movement and enhanced robot vision with a robust classification of objects and obstacles using vision sensor data in the indoor environment. However, state-of-the-art methods face degraded
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Yu, Hang, Jinhe Su, Yingchao Piao, et al. "Refined Voting and Scene Feature Fusion for 3D Object Detection in Point Clouds." Computational Intelligence and Neuroscience 2022 (December 29, 2022): 1–15. http://dx.doi.org/10.1155/2022/3023934.

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An essential task for 3D visual world understanding is 3D object detection in lidar point clouds. To predict directly bounding box parameters from point clouds, existing voting-based methods use Hough voting to obtain the centroid of each object. However, it may be difficult for the inaccurately voted centers to regress boxes accurately, leading to the generation of redundant bounding boxes. For objects in indoor scenes, there are several co-occurrence patterns for objects in indoor scenes. Concurrently, semantic relations between object layouts and scenes can be used as prior context to guide
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Heikel, Edvard, and Leonardo Espinosa-Leal. "Indoor Scene Recognition via Object Detection and TF-IDF." Journal of Imaging 8, no. 8 (2022): 209. http://dx.doi.org/10.3390/jimaging8080209.

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Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This pap
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Jia, Yin, Balakrishnan Ramalingam, Rajesh Elara Mohan, Zhenyuan Yang, Zimou Zeng, and Prabakaran Veerajagadheswar. "Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation." Sensors 23, no. 4 (2023): 2337. http://dx.doi.org/10.3390/s23042337.

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Hazardous object detection (escalators, stairs, glass doors, etc.) and avoidance are critical functional safety modules for autonomous mobile cleaning robots. Conventional object detectors have less accuracy for detecting low-feature hazardous objects and have miss detection, and the false classification ratio is high when the object is under occlusion. Miss detection or false classification of hazardous objects poses an operational safety issue for mobile robots. This work presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robo
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Dissertations / Theses on the topic "Indoor Object Detection"

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Grip, Linnea. "Vision based indoor object detection for a drone." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208890.

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Drones are a very active area of research and object detection is a crucial part in achieving full autonomy of any robot. We investigated how state-of-the-art object detection algorithms perform on image data from a drone. For the evaluation we collected a number of datasets in an indoor office environment with different cameras and camera placements. We surveyed the literature of object detection and selected to research the algorithm R-FCN (Region based Fully Convolutional Network) for the evaluation. The performances on the different datasets were then compared, showing that using footage f
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Jiang, Lixing [Verfasser]. "Object Recognition and Saliency Detection for Indoor Robots using RGB-D Sensors / Lixing Jiang." München : Verlag Dr. Hut, 2016. http://d-nb.info/1106593723/34.

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Ghorpade, Vijaya Kumar. "3D Semantic SLAM of Indoor Environment with Single Depth Sensor." Thesis, Université Clermont Auvergne‎ (2017-2020), 2017. http://www.theses.fr/2017CLFAC085/document.

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Pour agir de manière autonome et intelligente dans un environnement, un robot mobile doit disposer de cartes. Une carte contient les informations spatiales sur l’environnement. La géométrie 3D ainsi connue par le robot est utilisée non seulement pour éviter la collision avec des obstacles, mais aussi pour se localiser et pour planifier des déplacements. Les robots de prochaine génération ont besoin de davantage de capacités que de simples cartographies et d’une localisation pour coexister avec nous. La quintessence du robot humanoïde de service devra disposer de la capacité de voir comme les h
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Lee, Yeongseon. "Bayesian 3D multiple people tracking using multiple indoor cameras and microphones." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29668.

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Thesis (Ph.D)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: Rusell M. Mersereau; Committee Member: Biing Hwang (Fred) Juang; Committee Member: Christopher E. Heil; Committee Member: Georgia Vachtsevanos; Committee Member: James H. McClellan. Part of the SMARTech Electronic Thesis and Dissertation Collection.
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Kossonou, Kobenan Ignace. "Étude d'un système de localisation 3-D haute précision basé sur les techniques de transmission Ultra Large Bande à basse consommation d'énergie pour les objets mobiles communicants." Phd thesis, Université de Valenciennes et du Hainaut-Cambresis, 2014. http://tel.archives-ouvertes.fr/tel-01019504.

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Les systèmes de localisations existants présentent des insuffisances au niveau desapplications en environnement indoor. Ces insuffisances se traduisent soit par la non-disponibilité des signaux (le GPS) dans ce type d'environnement, soit par leur manque de précision quand ils sont prévus à cet effet. Ces limites ont motivé la recherche de nouvelles techniques. Les transmissions Ultra-Large Bande (ULB) de par leur singularité en matière de précision et de faible puissance d'émission, s'avèrent être la meilleure réponse à la problématique ci-dessus. Nous avons donc choisi cette technique pour me
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Wei-Hao, Tung. "Abandoned Object Detection for Indoor Public Surveillance Video." 2006. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0016-1303200709312360.

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Tung, Wei-Hao, and 童韋豪. "Abandoned Object Detection for Indoor Public Surveillance Video." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/54246948826637791625.

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碩士<br>國立清華大學<br>資訊工程學系<br>94<br>As the increasing of the bomb attacks in recent years and these attacks are repeatedly concentrated on the public places, such as MRT stations. Establishing a surveillance system with high-tech appliances to against terrorism becomes a critical issue nowadays. In this thesis, an algorithm of finding the abandoned objects in the environment of crowded public places is proposed. There exist some approaches to discover the abandoned objects under the circumstance of two scenarios: 1) The pixels in the scenes of abandoned objects do not intermixed with background pi
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Lahoud, Jean. "Indoor 3D Scene Understanding Using Depth Sensors." Diss., 2020. http://hdl.handle.net/10754/665033.

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One of the main goals in computer vision is to achieve a human-like understanding of images. Nevertheless, image understanding has been mainly studied in the 2D image frame, so more information is needed to relate them to the 3D world. With the emergence of 3D sensors (e.g. the Microsoft Kinect), which provide depth along with color information, the task of propagating 2D knowledge into 3D becomes more attainable and enables interaction between a machine (e.g. robot) and its environment. This dissertation focuses on three aspects of indoor 3D scene understanding: (1) 2D-driven 3D object detect
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Chang, Ren-Jie, and 張人傑. "Moving Object Detection and Shadow Elimination in a Dark Indoor Environment with Fixed Weak Lamplight." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/63881807714894541239.

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碩士<br>大葉大學<br>資訊管理學系碩士班<br>94<br>At present, the application of environment monitoring using video capturing and recording devices become popular day by day. Most of these equipments are installed in communities, shops, or building doorways. Besides, video recorders are also seen in several indoor environments such as train stations and banks. Several secret or private rooms need more accurate monitoring devices than those public spaces. The general monitoring device provides an investigation function after the event. Several intelligent video surveillance systems are developed to contribute a
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Book chapters on the topic "Indoor Object Detection"

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Chung, Youngsun, Daeyoung Gil, and Ghang Lee. "Optimal Number of Cue Objects for Photo-Based Indoor Localization." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.98.

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Building information modeling (BIM) is widely used to generate indoor images for indoor localization. However, changes in camera angles and indoor conditions mean that photos are much more changeable than BIM images. This makes any attempt at localization based on the similarity between real photos and BIM images challenging. To overcome this limitation, we propose a reasoning-based approach for determining the location of a photo by detecting the cue objects in the photo and the relationships between them. The aim of this preliminary study was to determine the optimal number of cue objects re
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Chung, Youngsun, Daeyoung Gil, and Ghang Lee. "Optimal Number of Cue Objects for Photo-Based Indoor Localization." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0289-3.98.

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Building information modeling (BIM) is widely used to generate indoor images for indoor localization. However, changes in camera angles and indoor conditions mean that photos are much more changeable than BIM images. This makes any attempt at localization based on the similarity between real photos and BIM images challenging. To overcome this limitation, we propose a reasoning-based approach for determining the location of a photo by detecting the cue objects in the photo and the relationships between them. The aim of this preliminary study was to determine the optimal number of cue objects re
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Braun, João, João Mendes, Ana I. Pereira, José Lima, and Paulo Costa. "Object Detection for Indoor Localization System." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-23236-7_54.

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Sharma, Anima M., Imran A. Syed, Bishwajit Sharma, Arshad Jamal, and Dipti Deodhare. "Visual Object Detection for an Autonomous Indoor Robotic System." In Proceedings of 2nd International Conference on Computer Vision & Image Processing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7895-8_17.

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Ma, Lichao, Licong Guan, and Yang Liu. "A Novel Indoor Object Detection Algorithm Under Complex Conditions." In Advances in Intelligent Automation and Soft Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81007-8_4.

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Qiao, Leixian, Yaohui Zhu, Runze Li, Weiqing Min, and Shuqiang Jiang. "Indoor RGB-D Object Detection with the Guidance of Hand-Held Objects." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8530-7_12.

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Kumar, N. Satish, and G. Shobha. "Background Modeling and Foreground Object Detection for Indoor Video Sequence." In Proceedings of the International Conference on Data Engineering and Communication Technology. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1678-3_77.

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Wang, Shilin, Hai Huang, Yueyan Zhu, and Zhenqi Tang. "CDAF3D: Cross-Dimensional Attention Fusion for Indoor 3D Object Detection." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8493-6_12.

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Zhou, Feng, Ju Dai, Junjun Pan, et al. "GFENet: Group-Free Enhancement Network for Indoor Scene 3D Object Detection." In Advances in Computer Graphics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50075-6_10.

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Shao, Bin, and Zhimin Yan. "3D Indoor Environment Modeling and Detection of Moving Object and Scene Understanding." In Transactions on Edutainment XIV. Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56689-3_4.

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Conference papers on the topic "Indoor Object Detection"

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Shu, Manli, Le Xue, Ning Yu, et al. "Hierarchical Point Attention for Indoor 3D Object Detection." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610108.

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Pensiri, Fuangfar, Phasuwut Chunnapiya, Wanida Khamprapai, and Porawat Visutsak. "Efficient Video Object Detection of Indoor Furniture and Home Appliances." In 2024 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). IEEE, 2024. http://dx.doi.org/10.1109/blackseacom61746.2024.10646241.

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Tatar, Doğan, Muhammet Emin Kahraman, and Selma Yılmazyıldız Kayaarma. "Object Detection-Based Indoor Earthquake Risk Analysis for NAO Robot." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10710807.

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Zhang, Yueqing, Zhi Yu, and Kuizhen Huang. "A Large-Scale Object Detection Method for Complex Indoor Traffic Scenarios." In 2025 IEEE 5th International Conference on Electronic Technology, Communication and Information (ICETCI). IEEE, 2025. https://doi.org/10.1109/icetci64844.2025.11084171.

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Mohan, Narendra, and Manoj Kumar. "Tuned YOLOv4 Model for Indoor 3D Object Detection from Point Cloud Data." In 2025 International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2025. https://doi.org/10.1109/iciccs65191.2025.10985198.

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Neware, Shubhangi, Himanshu Sharma, Dhyanesh Dharmik, Ayush Yadav, and Utkarsh Awasthi. "Object Detection in Indoor Environment to Assist Visually Impaired Individuals at Office Work." In 2024 OITS International Conference on Information Technology (OCIT). IEEE, 2024. https://doi.org/10.1109/ocit65031.2024.00044.

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Lan, Zixu, Fang Deng, Angang Zhang, and Zhongjian Chen. "MODCL: multi-modal object detection with end-to-end contrastive learning in indoor scene." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051405.

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Dikkatwar, Sai, Abhishek Mhaske, Harsh Ninawe, Harshit Khadia, and Shraddha S. Gugulothu. "Recent Trends in Low Light Object Detection in Indoor Spaces for Assisting the Visually Impaired." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914892.

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Borges, Eduardo, Luís Garrote, and Urbano Nunes. "A Modular Multimodal Multi-Object Tracking-by-Detection Approach, with Applications in Outdoor and Indoor Environments." In 21st International Conference on Informatics in Control, Automation and Robotics. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013073200003822.

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Shimizu, Shunsuke, Osamu Muta, Tomoki Murakami, Shinya Otsuki, and Ranae Otani. "Experimental Evaluation of WLAN-based Object Detection Using CSI in Outdoor and Large-scale Indoor Environments." In 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall). IEEE, 2024. https://doi.org/10.1109/vtc2024-fall63153.2024.10757785.

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