LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Atanbori, J; Duan, W; Murray, J; Appiah, K; Dickinson, P (2015)
Publisher: BMVA Press
Languages: English
Types: Part of book or chapter of book
Subjects:
Bird populations are an important bio-indicator; so collecting reliable data is useful for ecologists helping conserve and manage fragile ecosystems. However, existing manual monitoring methods are labour-intensive, time-consuming, and error-prone. The aim of our work is to develop a reliable system, capable of automatically classifying individual bird species in flight from videos. This is challenging, but appropriate for use in the field, since there is often a requirement to identify in flight, rather than when stationary. We present our work in progress, which uses combined appearance and motion features to classify and present experimental results across seven species using Normal Bayes classifier with majority voting and achieving a classification rate of 86%.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [2] John Atanbori, Peter Cowling, John Murray, Belinda Colston, Paul Eady, Dave Hughes, Ian Nixon, and Patrick Dickinson. Analysis of bat wing beat frequency using fourier transform. In Computer Analysis of Images and Patterns, pages 370-377. Springer, 2013.
    • [3] Faisal I Bashir, Ashfaq A Khokhar, and Dan Schonfeld. View-invariant motion trajectory-based activity classification and recognition. Multimedia Systems, 12(1): 45-54, 2006.
    • [4] Thomas Berg and Peter N Belhumeur. Poof: Part-based one-vs.-one features for finegrained categorization, face verification, and attribute estimation. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 955-962. IEEE, 2013.
    • [5] Thomas Berg, Jiongxin Liu, Seung Woo Lee, Michelle L Alexander, David W Jacobs, and Peter N Belhumeur. Birdsnap: Large-scale fine-grained visual categorization of birds. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 2019-2026. IEEE, 2014.
    • [6] Cigdem Beyan and Robert B Fisher. A filtering mechanism for normal fish trajectories. In Pattern Recognition (ICPR), 2012 21st International Conference On, pages 2286- 2289. IEEE, 2012.
    • [7] Cigdem Beyan and Robert B Fisher. Detection of abnormal fish trajectories using a clustering based hierarchical classifier. BMVC, Bristol, UK, 2013.
    • [8] Forrest Briggs, Balaji Lakshminarayanan, Lawrence Neal, Xiaoli Z Fern, Raviv Raich, Sarah JK Hadley, Adam S Hadley, and Matthew G Betts. Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach. The Journal of the Acoustical Society of America, 131(6):4640-4650, 2012.
    • [9] Stephen T Buckland, Stuart J Marsden, and Rhys E Green. Estimating bird abundance: making methods work. Bird Conservation International, 18(S1):S91-S108, 2008.
    • [10] Jia Deng, Jan Krause, and Li Fei-Fei. Fine-grained crowdsourcing for fine-grained recognition. In Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on, pages 580-587. IEEE, 2013.
    • [11] Ji-Xiang Du, Xiao-Feng Wang, and Guo-Jun Zhang. Leaf shape based plant species recognition. Applied mathematics and computation, 185(2):883-893, 2007.
    • [12] CA Duberstein, DJ Virden, S Matzner, J Myers, VI Cullinan, and AR Maxwell. Automated thermal image processing for detection and classification of birds and bats. 2012.
    • [13] Alison Johnston, Aonghais SCP Cook, Lucy J Wright, Elizabeth M Humphreys, and Niall HK Burton. Modelling flight heights of marine birds to more accurately assess collision risk with offshore wind turbines. Journal of Applied Ecology, 51(1):31-41, 2014.
    • [14] Alexis Joly, Hervé Goëau, Hervé Glotin, Concetto Spampinato, Pierre Bonnet, WillemPier Vellinga, Robert Planque, Andreas Rauber, Robert Fisher, and Henning Müller. Lifeclef 2014: multimedia life species identification challenges. In Information Access Evaluation. Multilinguality, Multimodality, and Interaction, pages 229-249. Springer, 2014.
    • [15] Ljubica Lazarevic, David Harrison, Darren Southee, Max Wade, and John Osmond. Wind farm and fauna interaction: detecting bird and bat wing beats through cyclic motion analysis. International Journal of Sustainable Engineering, 1(1):60-68, 2008.
    • [16] Xi Li, Weiming Hu, and Wei Hu. A coarse-to-fine strategy for vehicle motion trajectory clustering. In Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, volume 1, pages 591-594. IEEE, 2006.
    • [17] Marcelo T Lopes, Lucas L Gioppo, Thiago T Higushi, Celso AA Kaestner, Carlos N Silla Jr, and Alessandro L Koerich. Automatic bird species identification for large number of species. In Multimedia (ISM), 2011 IEEE International Symposium on, pages 117-122. IEEE, 2011.
    • [18] Andréia Marini, Jacques Facon, and Alessandro L Koerich. Bird species classification based on color features. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, pages 4336-4341. IEEE, 2013.
    • [19] Chi-Man Pun and Moon-Chuen Lee. Log-polar wavelet energy signatures for rotation and scale invariant texture classification. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(5):590-603, 2003.
    • [20] Muhammad Nawaz Rajpar and Mohamed Zakaria. Bird species abundance and their correlationship with microclimate and habitat variables at natural wetland reserve, peninsular malaysia. International Journal of Zoology, 2011, 2011.
    • [21] Szabolcs Sergyan. Color histogram features based image classification in content-based image retrieval systems. In Applied Machine Intelligence and Informatics, 2008. SAMI 2008. 6th International Symposium on, pages 221-224. IEEE, 2008.
    • [22] Carlos N Silla, Celso Kaestner, et al. Hierarchical classification of bird species using their audio recorded songs. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on, pages 1895-1900. IEEE, 2013.
    • [23] Concetto Spampinato, Daniela Giordano, Roberto Di Salvo, Yun-Heh Jessica ChenBurger, Robert Bob Fisher, and Gayathri Nadarajan. Automatic fish classification for underwater species behavior understanding. In Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams, pages 45-50. ACM, 2010.
    • [24] Satoshi Suzuki et al. Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing, 30(1):32-46, 1985.
    • [25] Lee N Tan, Abeer Alwan, George Kossan, Martin L Cody, and Charles E Taylor. Dynamic time warping and sparse representation classification for birdsong phrase classification using limited training dataa). The Journal of the Acoustical Society of America, 137(3):1069-1080, 2015.
    • [26] Catherine Wah, Steve Branson, Pietro Perona, and Serge Belongie. Multiclass recognition and part localization with humans in the loop. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2524-2531. IEEE, 2011.
    • [27] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011.
    • [28] Peter Welinder, Steve Branson, Takeshi Mita, Catherine Wah, Florian Schroff, Serge Belongie, and Pietro Perona. Caltech-ucsd birds 200. 2010.
    • [29] Bangpeng Yao, Gary Bradski, and Li Fei-Fei. A codebook-free and annotation-free approach for fine-grained image categorization. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 3466-3473. IEEE, 2012.
    • [30] Ning Zhang, Ryan Farrell, Forrest Iandola, and Trevor Darrell. Deformable part descriptors for fine-grained recognition and attribute prediction. In Computer Vision (ICCV), 2013 IEEE International Conference on, pages 729-736. IEEE, 2013.
    • [31] Zoran Zivkovic and Ferdinand van der Heijden. Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern recognition letters, 27 (7):773-780, 2006.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article