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Relevant bibliographies by topics / Small target tracking

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Academic literature on the topic 'Small target tracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

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Journal articles on the topic "Small target tracking"

1

Zheng, Wenjian, An Chang, Qi Wang, Jianing Shang, and Mandi Cui. "Correlation Filtering Algorithm of Infrared Spectral Data for Dim Target Tracking." Advances in Mathematical Physics 2023 (April 24, 2023): 1–10. http://dx.doi.org/10.1155/2023/1240426.

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The correlation filtering algorithm of infrared spectral data for dim and small target tracking is studied to improve the tracking accuracy of small and weak targets and to track small and weak targets in real time. After the image noise reduction processing by the mean shift filtering algorithm, the infrared small and weak target image data model is constructed by using the denoised infrared small and weak target image. And the brightness value and position of unknown small and weak targets are obtained. The tracking and measurement model of small and weak targets is built. The joint probabil

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Wang, Kaipeng, Zhijun Meng, and Zhe Wu. "Deep Learning-Based Ground Target Detection and Tracking for Aerial Photography from UAVs." Applied Sciences 11, no. 18 (2021): 8434. http://dx.doi.org/10.3390/app11188434.

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Target detection and tracking can be widely used in military and civilian scenarios. Unmanned aerial vehicles (UAVs) have high maneuverability and strong concealment, thus they are very suitable for using as a platform for ground target detection and tracking. Most of the existing target detection and tracking algorithms are aimed at conventional targets. Because of the small scale and the incomplete details of the targets in the aerial image, it is difficult to apply the conventional algorithms to aerial photography from UAVs. This paper proposes a ground target image detection and tracking a

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3

Li, Haiqing, Mengbo Yang, Yanbo Li, Liming Dai, and Chunhui Zhao. "Dynamic Target Tracking of Small UAVs in Unstructured Environment." Electronics 12, no. 5 (2023): 1078. http://dx.doi.org/10.3390/electronics12051078.

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In this paper, an adaptive multi-rotor UAV system of dynamic target tracking and path planning is proposed for the problems of occlusion, lighting change, and similar target interference in an unstructured environment. A DTE-tracker module is designed, consisting of a detector, tracker, and examiner, and proposes a dynamic target capture mechanism to improve the robustness and continuity of target tracking in complex environments. A DWA local path planning algorithm based on a dynamic target tracking task is proposed to control the yaw of the UAV to accurately locate specific targets in the ce

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Tian, Xiangrui, Yinjun Jia, Xin Luo, and Jie Yin. "Small Target Recognition and Tracking Based on UAV Platform." Sensors 22, no. 17 (2022): 6579. http://dx.doi.org/10.3390/s22176579.

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Target recognition and tracking based on multi-rotor UAVs have the advantages of low cost and high flexibility. It can monitor low-altitude targets with high intensity. It has great application prospects in national defense, military, and civil fields. The existing algorithms for aerial small target recognition and tracking have the disadvantages of slow speed, low accuracy, poor robustness, and insufficient intelligence. Aiming at the problems of existing algorithms, this paper first makes a lightweight improvement for the YOLOv4 network recognition algorithm suitable for small target recogni

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Fan, Jun, Jingbiao Wei, Hai Huang, Dafeng Zhang, and Ce Chen. "IRSDT: A Framework for Infrared Small Target Tracking with Enhanced Detection." Sensors 23, no. 9 (2023): 4240. http://dx.doi.org/10.3390/s23094240.

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Currently, infrared small target detection and tracking under complex backgrounds remains challenging because of the low resolution of infrared images and the lack of shape and texture features in these small targets. This study proposes a framework for infrared vehicle small target detection and tracking, comprising three components: full-image object detection, cropped-image object detection and tracking, and object trajectory prediction. We designed a CNN-based real-time detection model with a high recall rate for the first component to detect potential object regions in the entire image. T

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Zhu, Xinli, Tingting Kou, Mingzhi Si, and Shuai Wang. "Multi target tracking algorithm based on anchor free detection." Journal of Physics: Conference Series 2637, no. 1 (2023): 012050. http://dx.doi.org/10.1088/1742-6596/2637/1/012050.

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Abstract In order to alleviate the problems of frequent identity label switching caused by mutual occlusion between targets and missed detection caused by small target sizes in multi-target tracking, this paper proposes a multi-target tracking algorithm based on anchor-free detection. This algorithm first uses YOLOX for detection and embeds the ReID algorithm to enhance pedestrian recognition. Then, it uses the DeepSORT algorithm for tracking and correcting abnormal trajectory algorithms for occluded targets. Finally, experiments are conducted on both the MOT16 dataset and the self-made datase

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ZHAO, Qian, Zhenhong NIU, Lian XUE, Xin LIU, Jianhua LI, and Zhifeng LI. "Automatic MHT Method Based on Target Characteristics Applied in the Small Space Targets." MATEC Web of Conferences 237 (2018): 01017. http://dx.doi.org/10.1051/matecconf/201823701017.

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The Multi-Hypothesis Tracking (MHT) method is one of the most promising approaches to realize automatic tracking of small and dim targets in complex space scene. However, the number of hypotheses generated by this algorithm has an exponentially increasing relationship with the number of trajectories, measurement data, and the number of image frames. In the paper, the automatic detection and tracking method of judging the confidence coefficient of the trajectory obtained by the MHT which is based on the characteristics of dim targets, has been realized in the IRST system. The performance of thi

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Liang, Lei, Zhihua Chen, Lei Dai, and Shouli Wang. "Target signature network for small object tracking." Engineering Applications of Artificial Intelligence 138 (December 2024): 109445. http://dx.doi.org/10.1016/j.engappai.2024.109445.

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Bar-Shalom, Yaakov, Hemchandra M. Shertukde, and Krishna R. Pattipati. "Precision target tracking for small extended objects." Optical Engineering 29, no. 2 (1990): 121. http://dx.doi.org/10.1117/12.55580.

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Yang, Shuai, Zhihui Zou, Yingchao Li, Haodong Shi, and Qiang Fu. "Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images." Applied Sciences 13, no. 13 (2023): 7931. http://dx.doi.org/10.3390/app13137931.

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To address the issue of poor tracking accuracy and the low recognition rate for multiple small targets in infrared images caused by uneven image intensity, this paper proposes an accurate tracking algorithm based on optical flow estimation. The algorithm consists of several steps. Firstly, an infrared image subspace model is established. Secondly, a full convolutional network (FCN) is utilized for local double-threshold segmentation of the target image. Furthermore, a target observation model is established using SIR filtering particles. Lastly, a shift vector sum algorithm is employed to enha

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Dissertations / Theses on the topic "Small target tracking"

1

Prince, Robert A. "Autonomous visual tracking of stationary targets using small unmanned aerial vehicles." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FPrince.pdf.

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Johansen, David Linn. "Video Stabilization and Target Localization Using Feature Tracking with Video from Small UAVs." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1522.pdf.

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Saunders, Jeffery Brian. "Obstacle Avoidance, Visual Automatic Target Tracking, and Task Allocation for Small Unmanned Air Vehicles." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3028.pdf.

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DeFranco, Patrick. "Detecting and Tracking Moving Objects from a Small Unmanned Air Vehicle." BYU ScholarsArchive, 2015. https://scholarsarchive.byu.edu/etd/5311.

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As the market for unmanned air vehicles (UAVs) rapidly expands, the need for algorithmsthat improve the capabilities of those vehicles is also growing. One valuable capability for UAVsis that of persistent tracking—the ability to find and track another moving object, usually on theground, from an aerial platform. This thesis presents a method for tracking multiple ground targetsfrom an airborne camera. Moving objects on the ground are detected by using frame-to-frameregistration. The detected objects are then tracked using the newly developed recursive RANSACalgorithm. Much video tracking work

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Trago, Todd Michael. "Performance analysis for a vision-based target tracking system of a Small Unmanned Aerial Vehicle /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2005. http://library.nps.navy.mil/uhtbin/hyperion/05Sep%5FTrago.pdf.

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Thesis (M.S. in Engineering Science (Mechanical Engineering))--Naval Postgraduate School, September 2005.<br>Thesis Advisor(s): Isaac Kaminer. Includes bibliographical references (p. 67). Also available online.

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Trago, Todd M. "Performance analysis for a vision-based target tracking system of a Small Unmanned Aerial Vehicle." Thesis, Monterey California. Naval Postgraduate School, 2005. http://hdl.handle.net/10945/2099.

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This thesis analyzes performance of the vision-based target-tracking system developed at the Naval Postgraduate School using the Monte Carlo method. Specifically, sensitivities of the target position estimation algorithm to various sensor errors are computed and analyzed. Furthermore, dependence of this algorithm on the performance of the target-tracking control system is established.

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Morgan, Hayden Matthew. "Small-Target Detection and Observation with Vision-Enabled Fixed-Wing Unmanned Aircraft Systems." BYU ScholarsArchive, 2021. https://scholarsarchive.byu.edu/etd/8998.

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This thesis focuses on vision-based detection and observation of small, slow-moving targets using a gimballed fixed-wing unmanned aircraft system (UAS). Generally, visual tracking algorithms are tuned to detect motion of relatively large objects in the scene with noticeably significant motion; therefore, applications such as high-altitude visual searches for human motion often ignore target motion as noise. Furthermore, after a target is identified, arbitrary maneuvers for transitioning to overhead orbits for better observation may result in temporary or permanent loss of target visibility. W

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Tay, Boon Chong. "Development and implementation of new control law for vision based target tracking system onboard small Unmanned Aerial Vehicles." Thesis, Monterey, Calif. : Naval Postgraduate School, 2006. http://bosun.nps.edu/uhtbin/hyperion.exe/06Dec%5FTay.pdf.

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Thesis (M.S. in Engineering Science (Mechanical Engineering))--Naval Postgraduate School, December 2006.<br>Thesis Advisor(s): Isaac I. Kaminer, Vladimir N. Dobrokhodov. "December 2006." Includes bibliographical references (p. 89). Also available in print.

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Lusk, Parker Chase. "Vision-Based Emergency Landing of Small Unmanned Aircraft Systems." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7029.

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Emergency landing is a critical safety mechanism for aerial vehicles. Commercial aircraft have triply-redundant systems that greatly increase the probability that the pilot will be able to land the aircraft at a designated airfield in the event of an emergency. In general aviation, the chances of always reaching a designated airfield are lower, but the successful pilot might use landmarks and other visual information to safely land in unprepared locations. For small unmanned aircraft systems (sUAS), triply- or even doubly-redundant systems are unlikely due to size, weight, and power constraint

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Wikle, Jared Kevin. "Integration of a Complete Detect and Avoid System for Small Unmanned Aircraft Systems." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6361.

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For unmanned aircraft systems to gain full access to the National Airspace System (NAS), they must have the capability to detect and avoid other aircraft. This research focuses on the development of a detect-and-avoid (DAA) system for small unmanned aircraft systems. To safely avoid another aircraft, an unmanned aircraft must detect the intruder aircraft with ample time and distance. Two analytical methods for finding the minimum detection range needed are described. The first method, time-based geometric velocity vectors (TGVV), includes the bank-angle dynamics of the ownship while the second

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Books on the topic "Small target tracking"

1

Moran, Mark L. Source location and tracking capability of a small seismic array. U.S. Army Cold Regions Research and Engineering Laboratory, 1996.

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2

E, Drummond Oliver, and Society of Photo-optical Instrumentation Engineers., eds. Signal and data processing of small targets 1996: 9-11 April, 1996, Orlando, Florida. SPIE, 1996.

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E, Drummond Oliver, and Society of Photo-optical Instrumentation Engineers., eds. Signal and data processing of small targets 1994: 5-7 April 1994, Orlando, Florida. SPIE, 1994.

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4

E, Drummond Oliver, and Society of Photo-optical Instrumentation Engineers., eds. Signal and data processing of small targets 1997: 29-31 July 1997, San Diego, California. SPIE, 1997.

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5

Drummond, Oliver E. Signal and data processing of small targets 2010: 5-8 April 2010, Orlando, Florida, United States. Edited by SPIE (Society). SPIE, 2010.

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E, Drummond Oliver, and Society of Photo-optical Instrumentation Engineers., eds. Signal and data processing of small targets 1998: 14-16 April 1998, Orlando, Florida. SPIE, 1998.

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Drummond, Oliver E. Signal and data processing of small targets 2011: 23-25 August 2011, San Diego, California, United States. Edited by SPIE (Society). SPIE, 2011.

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E, Drummond Oliver, and Society of Photo-optical Instrumentation Engineers., eds. Signal and data processing of small targets 1993: 12-14 April 1993, Orlando, Florida. SPIE, 1993.

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E, Drummond Oliver, and Society of Photo-optical Instrumentation Engineers., eds. Signal and data processing of small targets 1993: 12-14 April 1993, Orlando, Florida. International Society for Optical Engineering, 1993.

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E, Drummond Oliver, Society of Photo-optical Instrumentation Engineers., and CREOL (Research center), eds. Signal and data processing of small targets, 1991: 1-3 April 1991, Orlando, Florida. SPIE, 1991.

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Book chapters on the topic "Small target tracking"

1

Jiang, Zhuoxu, Dawut Abdusalam, and Hamdulla Askar. "SiamMFT: Siamese MultiFrame Network in Infrared Small Target Tracking." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-9856-1_1.

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Zhang, Yujie, and Chao Mi. "A Fast Vision-Based Algorithm for Automated Container Pose Measurement System." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1876-4_64.

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AbstractAddressing the current issues of low accuracy in container positioning and posture recognition, as well as long response times during the port automation loading and unloading process, this paper designs a rapid container target recognition and measurement device and method for automated loading and unloading, thereby optimizing the acquisition of key parameters in automated loading and unloading operations. This method combines advanced convolutional neural networks and traditional image processing algorithms to achieve precise detection and tracking of container corner fittings. Furt

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Hou, Jun, Liming Dai, Chunhui Zhao, Xiaolei Hou, Jinwen Hu, and Yang Lyu. "Dynamic Target Tracking and Localization for Small UAV in Unstructured Outdoor Environment." In Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0479-2_19.

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Nguyen, Viet Duc. "Small-Target Radar Detection and Tracking within the PITAS Hard- and Software Environment." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33161-9_52.

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Li, Rui, Xinsheng Huang, Ruitao Lu, and Lurong Shen. "Infrared Small Target Tracking Algorithm Based on Fusion Feature Matching and Mean Shift Correction." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-1759-6_68.

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Chi, Yucan, Chengchao Bai, and Jifeng Guo. "Dynamic Small Target Detection and Tracking Based on Hierarchical Network and Adaptive Input Image Stream." In Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-9492-9_324.

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Quintero, Steven A. P., David A. Copp, and João P. Hespanha. "Robust Coordination of Small UAVs for Vision-Based Target Tracking Using Output-Feedback MPC with MHE." In Cooperative Control of Multi-Agent Systems. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781119266235.ch3.

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Watson, Geoffrey Alan, Kirsty Taylor, and Lillian L. Siu. "Innovation and Advances in Precision Medicine in Head and Neck Cancer." In Critical Issues in Head and Neck Oncology. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63234-2_24.

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AbstractThe clinical utility of precision medicine through molecular characterization of tumors has been demonstrated in some malignancies, especially in cases where oncogenic driver alterations are identified. Next generation sequencing data from thousands of patients with head and neck cancers have provided vast amounts of information about the genomic landscape of this disease. Thus far, only a limited number of genomic alterations have been druggable, such as NTRK gene rearrangements in salivary gland cancers (mainly mammary analogue secretory carcinoma), NOTCH mutations in adenoid cystic

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Tavakoli, Hooman, Sungho Suh, Snehal Walunj, Parsha Pahlevannejad, Christiane Plociennik, and Martin Ruskowski. "Object Detection for Human–Robot Interaction and Worker Assistance Systems." In Artificial Intelligence in Manufacturing. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46452-2_18.

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AbstractThe primary goal of this research is to describe the scenarios, challenges, and complexities associated with object detection in industrial environments and to provide clues on how to tackle them. While object detection in production lines offers significant advantages, it also poses notable difficulties. This chapter delves into the common scenarios and specific challenges encountered in industrial object detection and proposes targeted solutions for various use cases. For example, synthetic data play a pivotal role in overcoming labeling challenges, particularly when it comes to smal

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Guerrini, Federica. "Data-Informed Models for the Coupled Dispersal of Microplastics and Related Pollutants Applied to the Mediterranean Sea." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15374-7_1.

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AbstractMicroplastic pollution is a ubiquitous environmental threat, in particular to the oceans. In the marine environment, microplastics are not just passively transported by sea currents, but often get contaminated with organic pollutants during the journey. The uptake of chemicals onto microplastics can worsen the adverse effects of microplastics to marine organisms; however, investigation on this urgent phenomenon is hampered by the impossibility of monitoring and tracking such small plastic fragments during their motion at sea. This work aims at addressing the need for an effective model

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Conference papers on the topic "Small target tracking"

1

Yuan, Luo, Xiaorun Li, and Shuhan Chen. "Infrared small target detection based on clustering and tracking." In Conference on Spectral Technology and Applications (CSTA 2024), edited by Zhe Wang and Hongbin Ding. SPIE, 2024. https://doi.org/10.1117/12.3032517.

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Li, Fan, and Ze Chen. "Anti “low slow small” target detection, recognition and tracking algorithm." In 2025 5th International Symposium on Computer Technology and Information Science (ISCTIS). IEEE, 2025. https://doi.org/10.1109/isctis65944.2025.11066057.

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Dedic, E., D. Henry, M. Lihoreau, and H. Aubert. "3D Tracking of Small Moving Targets from a Multi-Target Tracking Algorithm Applied to Millimeter-wave Radar Images." In 2024 21st European Radar Conference (EuRAD). IEEE, 2024. http://dx.doi.org/10.23919/eurad61604.2024.10734870.

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Li, Dawei, Zhenbao Luo, Han Wen, Yuhang Li, and Hongyue Zha. "Confidence Evaluation Algorithm for Infrared Small Target Tracking in Complex Backgrounds." In 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2024. https://doi.org/10.1109/iccasit62299.2024.10827986.

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Li, Zixuan, Biao Jin, Kangsheng Sun, and Chao Liu. "Sea Surface Small Target Tracking Method with Integrated Dynamic Wave Prediction Information." In 2024 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2024. https://doi.org/10.1109/icsidp62679.2024.10868191.

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Li, Zhenyuan, Jing Liu, Jianting Zhang, et al. "SiamTBD: A Novel Algorithm for Small and Dim Target Tracking Using Video SAR." In 2024 43rd Chinese Control Conference (CCC). IEEE, 2024. http://dx.doi.org/10.23919/ccc63176.2024.10661438.

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Tian, Jin, Yun Zhang, Dai Gao, Haoxuan Yuan, Weimin Kong, and Long Li. "Weak and Small Group Target Tracking Method Based on Multi-Dimensional Screening of Point Traces." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642154.

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Du, Heng, Huaiming Cang, Yiwen Bai, and Xinyi Wang. "Small-Target Infrastructure Detection with Spatial Tracking and Multi-Level Feature Extraction of Railway Track Scenes." In 2024 International Conference on Intelligent Communication, Sensing and Electromagnetics (ICSE). IEEE, 2024. https://doi.org/10.1109/icse63502.2024.10899092.

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Minghui Xu and Lingjiang Kong. "Knowledge-aided small target tracking." In 2011 IEEE CIE International Conference on Radar (Radar). IEEE, 2011. http://dx.doi.org/10.1109/cie-radar.2011.6159926.

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Toumodge, S. S. "Target tracking by a human operator." In Signal and Data Processing of Small Targets 1990. SPIE, 1990. http://dx.doi.org/10.1117/12.2321778.

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Reports on the topic "Small target tracking"

1

Research, IFF. Small and Micro Food Business Operator (FBO) Tracking Survey: Wave 3 2021 - Technical Report. Food Standards Agency, 2022. http://dx.doi.org/10.46756/sci.fsa.sty242.

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The Food Standards Agency (FSA) has been tracking small and micro Food Business Operator (FBO) attitudes towards food-related topics, and trust in the FSA and food system, since 2018. This has helped inform engagement and intervention activity targeted at businesses with fewer than 50 staff. The survey was initially developed to assess the perceived impact of changes as a result of the UK’s exit from the European Union (EU), and the Achieving Business Compliance (ABC) programme, which aims to modernise the regulation of food businesses in England, Wales and Northern Ireland. Since then, it has

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Letcher, Theodore, Julie Parno, Taylor Hodgdon, Zoe Courville, and Jason Olivier. Applications of the CRREL-Geometric Optics Snow Radiative Transfer (GOSRT) model : incorporating diffraction and simulating detection of buried targets. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49728.

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Radiative transfer through a snow surface within the visible and near infrared (NIR) spectra is complicated by the shape, size, and configuration of the snow grains that comprise the snow surface. Ray-tracing and photon-tracking techniques combined with 3D renderings of snow resolved at the microscale have shown promise as a means to directly simulate radiative transfer through snow with no restrictions on the snow grain configuration. This report describes and evaluates the US Army Cold Regions Research and Engineering Laboratory (CRREL) Geometric Optics Snow Radiative Transfer (GOSRT) model.

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Jia, Lili, and Steve Evans. Prevent food allergy alerts: an incentive-based approach. Food Standards Agency, 2022. http://dx.doi.org/10.46756/sci.fsa.flm647.

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The majority of UK food recalls are due to allergen mislabelling, misleading allergen claims and/or the unintentional presence of allergens – representing a significant food safety risk and cost to industry. Labelling legislation must be followed to ensure food is safe and what it says it is, and this requires good allergen management and accurate allergen information communication down the supply chain. Distilling this information accurately, to inform labelling and/or communication of allergen information, can be particularly challenging for small to medium food businesses due to the low ado

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