ABSTRACT
Video-based insect tracking provides vital insights into insect behavior and ecology, enhancing our understanding of their movements and interactions. Therefore, examining trends in this field over the last few years is essential. This study aims to conduct a bibliometric analysis to unveil the growing interest in video-based insect tracking with a short review based on documents used for bibliometric analysis. To achieve this, 453 documents were extracted from Scopus on 12 June 2023. Only documents in English published between 2010 and 2023, resulting in a dataset of 318 documents, were analyzed. The findings illustrate a consistent growth in video-based insect research over the last years, with a significant peak in 2021, comprising 32 documents. The journal PLOS ONE stands out as the most productive source. The USA exhibited the most significant interest in video-based insect tracking over the last years. Keyword analysis reflects the multidisciplinary nature of insect tracking research. The review demonstrated that video-based insect tracking serves two primary objectives: pose estimation and trajectory information. However, the main challenge in video-based insect tracking is to preserve the identity of multiple individuals in situations involving occlusions or complex interactions.
- Andreas Aristidou and Joan Lasenby. 2011. FABRIK: A fast, iterative solver for the Inverse Kinematics problem. Graphical Models 73, 5 (2011), 243–260. https://doi.org/10.1016/j.gmod.2011.05.003Google ScholarDigital Library
- Yufang Bao and Hamid Krim. 2018. Video Tracking of Insect Flight Path: Towards Behavioral Assessment. (2018), 1–6. https://doi.org/10.1109/IPTA.2018.8608167Google ScholarCross Ref
- Omri Ben-Dov and Tsevi Beatus. 2021. Pose estimation of free-flying fruit flies. bioRxiv (2021), 2021–01. https://doi.org/10.1101/2021.01.24.427941Google ScholarCross Ref
- Attila J. Bergou, Leif Ristroph, John Guckenheimer, Itai Cohen, and Z. Jane Wang. 2010. Fruit Flies Modulate Passive Wing Pitching to Generate In-Flight Turns. Phys. Rev. Lett. 104 (04 2010), 148101. Issue 14. https://doi.org/10.1103/PhysRevLett.104.148101Google ScholarCross Ref
- Bibliometrix. [n. d.]. Bibliometrix. Accessed: 12 June 2023.Google Scholar
- Kim Bjerge, Hjalte MR Mann, and Toke Thomas Høye. 2022. Real-time insect tracking and monitoring with computer vision and deep learning. Remote Sensing in Ecology and Conservation 8, 3 (2022), 315–327. https://doi.org/10.1002/rse2.245Google ScholarCross Ref
- Kim Bjerge, Jakob Bonde Nielsen, Martin Videbæk Sepstrup, Flemming Helsing-Nielsen, and Toke Thomas Høye. 2020. An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning. bioRxiv (2020). https://doi.org/10.1101/2020.03.18.996447Google ScholarCross Ref
- Kim Bjerge, Jakob Bonde Nielsen, Martin Videbæk Sepstrup, Flemming Helsing-Nielsen, and Toke Thomas Høye. 2021. An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning. Sensors 21, 2 (2021). https://doi.org/10.3390/s21020343Google ScholarCross Ref
- Andreas Brückner, Jacques Duparré, Robert Leitel, Peter Dannberg, Andreas Bräuer, and Andreas Tünnermann. 2010. Thin wafer-level camera lenses inspired by insect compound eyes. Opt. Express 18, 24 (11 2010), 24379–24394. https://doi.org/10.1364/OE.18.024379Google ScholarCross Ref
- Marjolein Bruijning, Marco D Visser, Caspar A Hallmann, and Eelke Jongejans. 2018. trackdem: Automated particle tracking to obtain population counts and size distributions from videos in r. Methods in Ecology and Evolution 9, 4 (2018), 965–973. https://doi.org/10.1111/2041-210X.12975Google ScholarCross Ref
- Xiaoyan Cao, Shihui Guo, Juncong Lin, Wenshu Zhang, and Minghong Liao. 2020. Online tracking of ants based on deep association metrics: method, dataset and evaluation. Pattern Recognition 103 (2020), 107233. https://doi.org/10.1016/j.patcog.2020.107233Google ScholarCross Ref
- Cheng Siong Chin, Aloysius Bo Hui Neo, and Simon See. 2022. Visual Marine Debris Detection using Yolov5s for Autonomous Underwater Vehicle. In 2022 IEEE/ACIS 22nd International Conference on Computer and Information Science (ICIS). 20–24. https://doi.org/10.1109/ICIS54925.2022.9882484Google ScholarCross Ref
- Mary Fletcher, Anna Dornhaus, and Min C. Shin. 2011. Multiple ant tracking with global foreground maximization and variable target proposal distribution. (2011), 570–576. https://doi.org/10.1109/WACV.2011.5711555Google ScholarDigital Library
- Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig. 2016. Virtual Worlds as Proxy for Multi-Object Tracking Analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).Google Scholar
- Asaf Gal, Jonathan Saragosti, and Daniel JC Kronauer. 2020. anTraX, a software package for high-throughput video tracking of color-tagged insects. eLife 9 (11 2020), e58145. https://doi.org/10.7554/eLife.58145Google ScholarCross Ref
- James R. Hagler and Charles G. Jackson. 2001. Methods for Marking Insects: Current Techniques and Future Prospects. Annual Review of Entomology 46, 1 (2001), 511–543. https://doi.org/10.1146/annurev.ento.46.1.511Google ScholarCross Ref
- David W. Hagstrum, Paul W. Flinn, and Dennis Shuman. 1996. Automated Monitoring Using Acoustical Sensors for Insects in Farm-Stored Wheat. Journal of Economic Entomology 89, 1 (02 1996), 211–217. https://doi.org/10.1093/jee/89.1.211Google ScholarCross Ref
- Sangchul Hwang and Young Choi. 2015. Tracking the joints of arthropod legs using multiple images and inverse kinematics. International Journal of Precision Engineering and Manufacturing 16 (2015), 669–675. https://doi.org/10.1007/s12541-015-0089-yGoogle ScholarCross Ref
- Chanyoung Ju and Hyoung Il Son. 2022. Investigation of an Autonomous Tracking System for Localization of Radio-Tagged Flying Insects. IEEE Access 10 (2022), 4048–4062. https://doi.org/10.1109/ACCESS.2022.3140488Google ScholarCross Ref
- Pierre Karashchuk, Katie L Rupp, Evyn S Dickinson, Sarah Walling-Bell, Elischa Sanders, Eiman Azim, Bingni W Brunton, and John C Tuthill. 2021. Anipose: a toolkit for robust markerless 3D pose estimation. Cell reports 36, 13 (2021). https://doi.org/10.1016/j.celrep.2021.109730Google ScholarCross Ref
- Luis Javier Linares-Sánchez, José Luis Fernández-Alemán, Ginés García-Mateos, Angel Pérez-Ruzafa, and Francisco Javier Sánchez-Vázquez. 2015. Follow-me: A new start-and-stop method for visual animal tracking in biology research. (2015), 755–758. https://doi.org/10.1109/EMBC.2015.7318472Google ScholarCross Ref
- Ahmed Nejmedine Machraoui, Mohamed Fethi Diouani, Jamila Ghrab, and Mounir Sayadi. 2014. Accurate detection and complete shape extraction of sand-flies using Gaussian mixture model. (2014), 1–6. https://doi.org/10.1109/IPAS.2014.7043277Google ScholarCross Ref
- Ahmed Nejmedine Machraoui, Mohamed Fethi Diouani, Jamila Ghrab, and Mounir Sayadi. 2016. Automated detection and tracking of phlebotominaes. (2016), 206–211. https://doi.org/10.1109/ATSIP.2016.7523069Google ScholarCross Ref
- Ahmed Nejmedine Machraoui, Mohamed Fethi Diouani, Aymen Mouelhi, Kaouther Jaouadi, Jamila Ghrab, Hafedh Abdelmelek, and Mounir Sayadi. 2019. Automatic identification and behavioral analysis of phlebotomine sand flies using trajectory features. The Visual Computer 35 (2019), 721–738. https://doi.org/10.1007/s00371-018-1506-xGoogle ScholarDigital Library
- Madah-Ul-Mustafa and Yu Zhu Liang. 2021. A Robust Object Tracking Method for Surveillance Applications to Handle Occlusion. 13, 1 (2021), 9–16. https://doi.org/10.7763/IJCTE.2021.V13.1283Google ScholarCross Ref
- Khan Malik M., Awan Tayyab W., Kim Intaek, and Soh Youngsung. 2014. Tracking Occluded Objects Using Kalman Filter and Color Information. 6, 5 (2014), 438–442. https://doi.org/10.7763/IJCTE.2014.V6.905Google ScholarCross Ref
- Julian Marstaller, Frederic Tausch, and Simon Stock. 2019. DeepBees - Building and Scaling Convolutional Neuronal Nets For Fast and Large-Scale Visual Monitoring of Bee Hives. (2019), 271–278. https://doi.org/10.1109/ICCVW.2019.00036Google ScholarCross Ref
- Bernardo Miranda, Joaquin Salas, and Pablo Vera. 2012. Bumblebees detection and tracking. In Workshop Vis. Observation Anal. Anim. Insect Behav. ICPR. 1–4.Google Scholar
- Ana Moreno-Delafuente, Elisa Garzo, Aranzazu Moreno, and Alberto Fereres. 2013. A Plant Virus Manipulates the Behavior of Its Whitefly Vector to Enhance Its Transmission Efficiency and Spread. PLOS ONE 8, 4 (04 2013), 1–10. https://doi.org/10.1371/journal.pone.0061543Google ScholarCross Ref
- Veronica Panadeiro, Alvaro Rodriguez, Jason Henry, Donald Wlodkowic, and Magnus Andersson. 2021. A review of 28 free animal-tracking software applications: current features and limitations. Lab animal 50, 9 (2021), 246–254. https://doi.org/10.1038/s41684-021-00811-1Google ScholarCross Ref
- Talmo D Pereira, Diego E Aldarondo, Lindsay Willmore, Mikhail Kislin, Samuel S-H Wang, Mala Murthy, and Joshua W Shaevitz. 2019. Fast animal pose estimation using deep neural networks. Nature methods 16, 1 (2019), 117–125. https://doi.org/10.1038/s41592-018-0234-5Google ScholarCross Ref
- Alfonso Pérez-Escudero, Julián Vicente-Page, Robert C Hinz, Sara Arganda, and Gonzalo G De Polavieja. 2014. idTracker: tracking individuals in a group by automatic identification of unmarked animals. Nature methods 11, 7 (2014), 743–748. https://doi.org/10.1038/nmeth.2994Google ScholarCross Ref
- Sabita Ranabhat, Kun Yan Zhu, Georgina V Bingham, and III Morrison, William R. 2022. Mobility of Phosphine-Susceptible and -Resistant Rhyzopertha dominica (Coleoptera: Bostrichidae) and Tribolium castaneum (Coleoptera: Tenebrionidae) After Exposure to Controlled Release Materials With Existing and Novel Active Ingredients. Journal of Economic Entomology 115, 3 (04 2022), 888–903. https://doi.org/10.1093/jee/toac033 arXiv:https://academic.oup.com/jee/article-pdf/115/3/888/43977610/toac033.pdfGoogle ScholarCross Ref
- Malika Nisal Ratnayake, Don Chathurika Amarathunga, Asaduz Zaman, Adrian G Dyer, and Alan Dorin. 2023. Spatial monitoring and insect behavioural analysis using computer vision for precision pollination. International Journal of Computer Vision 131, 3 (2023), 591–606. https://doi.org/10.1007/s11263-022-01715-4Google ScholarDigital Library
- Malika Nisal Ratnayake, Adrian G. Dyer, and Alan Dorin. 2021. Tracking individual honeybees among wildflower clusters with computer vision-facilitated pollinator monitoring. PLOS ONE 16, 2 (02 2021), 1–20. https://doi.org/10.1371/journal.pone.0239504Google ScholarCross Ref
- Alvaro Rodriguez, Hanqing Zhang, Jonatan Klaminder, Tomas Brodin, Patrik L Andersson, and Magnus Andersson. 2018. ToxTrac: a fast and robust software for tracking organisms. Methods in Ecology and Evolution 9, 3 (2018), 460–464. https://doi.org/10.1111/2041-210X.12874Google ScholarCross Ref
- Danish Shakeel, Gursimran Bakshi, and Dr Bhupinder Singh. 2020. Insect Detection and Flight Tracking in a Controlled Environment Using Machine Vision: Review of Existing Techniques and an Improved Approach. In Proceedings of the International Conference on Innovative Computing and Communications (ICICC) 2020. SSRN, 58–70.Google ScholarCross Ref
- Minmin Shen, Chen Li, Wei Huang, Paul Szyszka, Kimiaki Shirahama, Marcin Grzegorzek, Dorit Merhof, and Oliver Deussen. 2015. Interactive tracking of insect posture. Pattern Recognition 48, 11 (2015), 3560–3571. https://doi.org/10.1016/j.patcog.2015.05.011Google ScholarDigital Library
- Minmin Shen, Paul Szyszka, C. Giovanni Galizia, and Dorit Merhof. 2013. Automatic framework for tracking honeybee’s antennae and mouthparts from low framerate video. (2013), 4112–4116. https://doi.org/10.1109/ICIP.2013.6738847Google ScholarCross Ref
- Pudith Sirigrivatanawong and Koichi Hashimoto. 2016. Multiple Drosophila tracking with heading direction in crossover and touching scenarios. (2016), 1954–1959. https://doi.org/10.1109/ROBIO.2016.7866615Google ScholarDigital Library
- Michael Thomas Smith, Michael Livingstone, and Richard Comont. 2021. A method for low-cost, low-impact insect tracking using retroreflective tags. Methods in Ecology and Evolution 12, 11 (2021), 2184–2195. https://doi.org/10.1111/2041-210X.13699Google ScholarCross Ref
- R Studio. [n. d.]. RStudio Desktop. Accessed: 12 June 2023.Google Scholar
- Karolien van Nunen, Jie Li, Genserik Reniers, and Koen Ponnet. 2018. Bibliometric analysis of safety culture research. Safety Science 108 (2018), 248–258. https://doi.org/10.1016/j.ssci.2017.08.011Google ScholarCross Ref
- Nicolai Wojke, Alex Bewley, and Dietrich Paulus. 2017. Simple online and realtime tracking with a deep association metric. (2017), 3645–3649. https://doi.org/10.1109/ICIP.2017.8296962Google ScholarDigital Library
Index Terms
- Advancements in Video-Based Insect Tracking: A Bibliometric Analysis to A Short Survey
Recommendations
A bibliometric analysis of pharmacology and pharmacy journals: Scopus versus Web of Science
Our study examines the suitability of Scopus for bibliometric analyses in comparison with the Web of Science (WOS). In particular we want to explore if the outcome of bibliometric analyses differs ...
Bibliometric analysis of Nigeria's social science and arts and humanities publications in Thomson Scientific databases
Purpose -- This paper seeks to analyse publications on Nigeria indexed in Arts and Humanities Citation Index (AHCI) and Social Science Citation Index (SSCI) of Thomson Scientific databases respectively to understand the international perspective of ...
Citation analysis and bibliometric approach for ant colony optimization from 1996 to 2010
To build awareness of the development of ant colony optimization (ACO), this study clarifies the citation and bibliometric analysis of research publications of ACO during 1996-2010. This study analysed 12,960 citations from a total of 1372 articles ...
Comments