{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:35:17Z","timestamp":1776882917668,"version":"3.51.2"},"reference-count":28,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T00:00:00Z","timestamp":1668384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006245","name":"Ministry of Science and Technology","doi-asserted-by":"publisher","award":["MOST 109-2221-E-224-042, Taiwan."],"award-info":[{"award-number":["MOST 109-2221-E-224-042, Taiwan."]}],"id":[{"id":"10.13039\/501100006245","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Current surveillance systems frequently use fixed-angle cameras and record a feed from those cameras. There are several disadvantages to such systems, including a low resolution for far away objects, a limited frame range and wasted disk space. This paper presents a novel algorithm for automatically detecting, tracking and zooming in on active targets. The object tracking system is connected to a camera that has a 360\u00b0 horizontal and 90\u00b0 vertical movement range. The combination of tracking, movement identification and zoom means that the system is able to effectively improve the resolution of small or distant objects. The object detection system allows for the disk space to be conserved as the system ceases recording when no valid targets are detected. Using an adaptive object segmentation algorithm, it is possible to detect the shape of moving objects efficiently. When processing multiple targets, each target is assigned a color and is treated separately. The tracking algorithm is able to adapt to targets moving at different speeds and is able to control the camera according to a predictive formula to prevent the loss of image quality due to camera trail. In the test environment, the zoom can sufficiently lock onto the head of a moving human; however, simultaneous tracking and zooming occasionally results in a failure to track. If this system is deployed with a facial recognition algorithm, the recognition accuracy can be effectively improved.<\/jats:p>","DOI":"10.3390\/s22228791","type":"journal-article","created":{"date-parts":[[2022,11,15]],"date-time":"2022-11-15T02:36:40Z","timestamp":1668479800000},"page":"8791","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Intelligent Object Tracking with an Automatic Image Zoom Algorithm for a Camera Sensing Surveillance System"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9828-0773","authenticated-orcid":false,"given":"Shih-Chang","family":"Hsia","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu City, Yunlin County 64002, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3889-1764","authenticated-orcid":false,"given":"Szu-Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu City, Yunlin County 64002, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chung-Mao","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu City, Yunlin County 64002, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9476-8130","authenticated-orcid":false,"given":"Chuan-Yu","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, National Yunlin University of Science and Technology, Douliu City, Yunlin County 64002, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"170457","DOI":"10.1109\/ACCESS.2019.2955387","article-title":"A Systematic Review of Intelligence Video Surveillance: Trends, Techniques, Frameworks, and Datasets","volume":"7","author":"Shidik","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.patcog.2017.09.009","article-title":"Real-time nonpara metric background subtraction with tracking-based foreground update","volume":"74","author":"Cuevas","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"126700","DOI":"10.1109\/ACCESS.2020.3008262","article-title":"Kasabov Change Detection in Multitemporal Monitoring Images Under Low Illumination","volume":"8","author":"Zhu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"19516","DOI":"10.1109\/JSEN.2021.3091018","article-title":"Enhancing the Surveillance Detection Range of Image Sensors Using HDR Techniques","volume":"21","author":"Purohit","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4746","DOI":"10.1109\/JSTARS.2019.2957484","article-title":"Ground-Based Hyperspectral Image Surveillance Systems for Explosive Detection: Part I\u2014State of the Art and Challenges","volume":"12","author":"Koz","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107550","DOI":"10.1109\/ACCESS.2019.2931820","article-title":"Particle Filter-Based Prediction for Anomaly Detection in Automatic Surveillance","volume":"7","author":"Gao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2204","DOI":"10.1109\/TITS.2019.2917560","article-title":"Rapid and Robust Background Modeling Technique for Low-Cost Road Traffic Surveillance Systems","volume":"21","author":"Garg","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6541","DOI":"10.1109\/TII.2019.2921652","article-title":"Robust Image Hashing Based Efficient Authentication for Smart Industrial Environment","volume":"15","author":"Sajjad","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/TCE.2017.014979","article-title":"Fast scene analysis for surveillance & video databases","volume":"63","author":"Javanbakhti","year":"2017","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7889","DOI":"10.1109\/TIP.2021.3108405","article-title":"An Unified Recurrent Video Object Segmentation Framework for Various Surveillance Environments","volume":"30","author":"Patil","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/TITS.2018.2888698","article-title":"Cost-Effective Vehicle Type Recognition in Surveillance Images with Deep Active Learning and Web Data","volume":"21","author":"Huang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1109\/TIP.2018.2878349","article-title":"A Richly Annotated Pedestrian Dataset for Person Retrieval in Real Surveillance Scenarios","volume":"28","author":"Li","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2591","DOI":"10.1109\/TCSVT.2016.2589879","article-title":"Cost-effective active learning for deep image classification","volume":"27","author":"Wang","year":"2016","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6077","DOI":"10.1109\/TIP.2019.2922095","article-title":"Foreground Gating and Background Refining Network for Surveillance Object Detection","volume":"28","author":"Fu","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"159864","DOI":"10.1109\/ACCESS.2020.3020818","article-title":"Foreground Objects Detection Using a Fully Convolutional Network with a Background Model Image and Multiple Original Images","volume":"8","author":"Kim","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10976","DOI":"10.1109\/ACCESS.2019.2891943","article-title":"Illumination-aware multi-task GANs for foreground segmentation","volume":"7","author":"Sakkos","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.patcog.2017.09.040","article-title":"A deep convolutional neural network for video sequence background subtraction","volume":"76","author":"Babaee","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14567","DOI":"10.1109\/ACCESS.2018.2803787","article-title":"From Eyes to Face Synthesis: A New Approach for Human-Centered Smart Surveillance","volume":"6","author":"Chen","year":"2018","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1109\/LRA.2021.3057003","article-title":"GridNet: Image-Agnostic Conditional Anomaly Detection for Indoor Surveillance","volume":"6","author":"Bozcan","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"63283","DOI":"10.1109\/ACCESS.2021.3074319","article-title":"Hawk-Eye: An AI-Powered Threat Detector for Intelligent Surveillance Cameras","volume":"9","author":"Ahmed","year":"2021","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/TCSVT.2019.2897980","article-title":"Saliency-Aware Convolution Neural Network for Ship Detection in Surveillance Video","volume":"30","author":"Shao","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1007\/s11554-020-00958-z","article-title":"Research and implementation of multi-object tracking based on vision DSP","volume":"17","author":"Gong","year":"2020","journal-title":"J. Real-Time Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s11554-020-01050-2","article-title":"Enhanced TLD-based video object-tracking implementation tested on embedded platforms","volume":"18","author":"Otoom","year":"2021","journal-title":"J. Real Time Image Process."},{"key":"ref_24","unstructured":"(2022, September 10). Available online: https:\/\/www.vdicctv.com\/."},{"key":"ref_25","unstructured":"(2022, November 10). Available online: https:\/\/www.advantech.com\/zh-tw\/products\/video-vision-cards\/sub_multi-core_digital_signal_processing."},{"key":"ref_26","unstructured":"(2022, August 20). Available online: http:\/\/www.youtube.com\/watch?v=iSC8VFOV0zc&feature=youtu.be."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1049\/iet-ipr.2018.6175","article-title":"Fast Search Real-Time Face Recognition based on DCT Coefficients Distribution","volume":"14","author":"Hsia","year":"2020","journal-title":"IET Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"177131","DOI":"10.1109\/ACCESS.2020.3024926","article-title":"MuLViS: Multi-Level Encryption Based Security System for Surveillance Videos","volume":"8","author":"Shifa","year":"2020","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8791\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:17:47Z","timestamp":1760145467000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/22\/8791"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,14]]},"references-count":28,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22228791"],"URL":"https:\/\/doi.org\/10.3390\/s22228791","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,14]]}}}