{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:30:50Z","timestamp":1760149850711,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T00:00:00Z","timestamp":1694822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program \u201cActive Health and Aging Science and Technology Response\u201d Special Project","award":["2020YFC2008503","AD22080004"],"award-info":[{"award-number":["2020YFC2008503","AD22080004"]}]},{"name":"Guangxi Science and Technology Base and Talent Project","award":["2020YFC2008503","AD22080004"],"award-info":[{"award-number":["2020YFC2008503","AD22080004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the context of predicting pedestrian trajectories for indoor mobile robots, it is crucial to accurately measure the distance between indoor pedestrians and robots. This study aims to address this requirement by extracting pedestrians as regions of interest and mitigating issues related to inaccurate depth camera distance measurements and illumination conditions. To tackle these challenges, we focus on an improved version of the H-GrabCut image segmentation algorithm, which involves four steps for segmenting indoor pedestrians. Firstly, we leverage the YOLO-V5 object recognition algorithm to construct detection nodes. Next, we propose an enhanced BIL-MSRCR algorithm to enhance the edge details of pedestrians. Finally, we optimize the clustering features of the GrabCut algorithm by incorporating two-dimensional entropy, UV component distance, and LBP texture feature values. The experimental results demonstrate that our algorithm achieves a segmentation accuracy of 97.13% in both the INRIA dataset and real-world tests, outperforming alternative methods in terms of sensitivity, missegmentation rate, and intersection-over-union metrics. These experiments confirm the feasibility and practicality of our approach. The aforementioned findings will be utilized in the preliminary processing of indoor mobile robot pedestrian trajectory prediction and enable path planning based on the predicted results.<\/jats:p>","DOI":"10.3390\/s23187937","type":"journal-article","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T23:57:46Z","timestamp":1694995066000},"page":"7937","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An H-GrabCut Image Segmentation Algorithm for Indoor Pedestrian Background Removal"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1316-717X","authenticated-orcid":false,"given":"Xuchao","family":"Huang","sequence":"first","affiliation":[{"name":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"},{"name":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6868-6542","authenticated-orcid":false,"given":"Shigang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"},{"name":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China"}]},{"given":"Xueshan","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"},{"name":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China"}]},{"given":"Dingji","family":"Luo","sequence":"additional","affiliation":[{"name":"Mechanical and Electrical College, Beijing Institute of Technology, Beijing 100190, China"}]},{"given":"Weiye","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"},{"name":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China"}]},{"given":"Huiqing","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China"},{"name":"Key Laboratory of Intelligent Sensing and Control, Liuzhou 545000, China"}]},{"given":"Ming","family":"Zhou","sequence":"additional","affiliation":[{"name":"Hangke Jinggong Co., Ltd., Beijing 102400, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.procs.2022.12.111","article-title":"Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning","volume":"216","author":"Hayati","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18751","DOI":"10.1007\/s11042-018-7022-1","article-title":"Resolution and quality enhancement of images using interpolation and contrast limited adaptive histogram equalization","volume":"78","author":"Aboshosha","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1007\/s10278-021-00447-0","article-title":"A hybrid algorithm to enhance colour retinal fundus images using a Wiener filter and CLAHE","volume":"34","author":"Alwazzan","year":"2021","journal-title":"J. Digit. Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10916-020-01568-9","article-title":"Fuzzy gray level difference histogram equalization for medical image enhancement","volume":"44","author":"Subramani","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6807","DOI":"10.1109\/TIM.2020.2976279","article-title":"Salp swarm algorithm-based optimally weighted histogram framework for image enhancement","volume":"69","author":"Bhandari","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","first-page":"1624","article-title":"A Retinex image enhancement algorithm based on image fusion technology","volume":"40","author":"Chang","year":"2018","journal-title":"Comput. Eng. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"LIME: Low-light image enhancement via illumination map estimation","volume":"26","author":"Guo","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1109\/TIP.2019.2958144","article-title":"A novel retinex-based fractional-order variational model for images with severely low light","volume":"29","author":"Gu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1109\/TIP.2018.2810539","article-title":"Structure-revealing low-light image enhancement via robust retinex model","volume":"27","author":"Li","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5679","DOI":"10.1109\/TIP.2019.2922106","article-title":"Low-light image enhancement via the absorption light scattering model","volume":"28","author":"Wang","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fan, Z., Liu, K., and Hou, J. (2023). JAUNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes. Appl. Sci., 13.","DOI":"10.3390\/app13031493"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liang, C., Xiao, B., and Cheng, B. (2022). XANet: An Efficient Remote Sensing Image Segmentation Model Using Element-Wise Attention Enhancement and Multi-Scale Attention Fusion. Remote Sens., 15.","DOI":"10.3390\/rs15010236"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, Y., and Zhang, Q. (2023, January 24\u201326). Semantic Segmentation of Traffic Scene Based on DeepLabv3+ and Attention Mechanism. Proceedings of the 2023 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE), Guangzhou, China.","DOI":"10.1109\/NNICE58320.2023.10105805"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"39275","DOI":"10.1007\/s11042-022-13026-4","article-title":"A novel visible spectrum images-based pedestrian detection and tracking system for surveillance in non-controlled environments","volume":"81","author":"Lahmyed","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"12622","DOI":"10.1109\/TITS.2021.3115705","article-title":"Multi-scale fusion with matching attention model: A novel decoding network cooperated with NAS for real-time semantic segmentation","volume":"23","author":"Xie","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","unstructured":"Boykov, Y.Y., and Jolly, M.P. (2001, January 7\u201314). Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. Proceedings of the Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vancouver, BC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"\u201cGrabCut\u201d interactive foreground extraction using iterated graph cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"ref_18","unstructured":"Yao, G., Wu, S., and Yang, H. (2021). Business Intelligence and Information Technology, Proceedings of the International Conference on Business Intelligence and Information Technology BIIT 2021, Harbin, China, 18\u201320 December 2021, Springer."},{"key":"ref_19","unstructured":"Prabu, S. (2022, January 24\u201326). Object segmentation based on the integration of adaptive K-means and GrabCut algorithm. Proceedings of the 2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), Chennai, India."},{"key":"ref_20","first-page":"43","article-title":"An improved segmentation algorithm based on GrabCut","volume":"40","author":"Wang","year":"2021","journal-title":"Inf. Technol. Netw. Secur."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"\u00dcnver, H.M., and Ayan, E. (2019). Skin lesion segmentation in dermoscopic images with combination of YOLO and grabcut algorithm. Diagnostics, 9.","DOI":"10.3390\/diagnostics9030072"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1109\/83.597272","article-title":"A multiscale retinex for bridging the gap between color images and the human observation of scenes","volume":"6","author":"Jobson","year":"1997","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, J., Han, D., and Wang, X. (2023). Multi-sensor medical-image fusion technique based on embedding bilateral filter in least squares and salient detection. Sensors, 23.","DOI":"10.3390\/s23073490"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5563698","DOI":"10.1155\/2021\/5563698","article-title":"Weak-light image enhancement method based on adaptive local gamma transform and color compensation","volume":"2021","author":"Wang","year":"2021","journal-title":"J. Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7937\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:52:17Z","timestamp":1760129537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/18\/7937"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,16]]},"references-count":24,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["s23187937"],"URL":"https:\/\/doi.org\/10.3390\/s23187937","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,9,16]]}}}