Automatic Animal Detection - UAVs in Environmental Research Conference
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Automatic Animal Detection
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Automatic Animal Detection - UAVs in Environmental Research Conference
- 1. Automatic Animal Detection CEO | camiel@dutchuas.nl |@camiel_v @dutchuasCamiel R. Verschoor
- 2. Wildlife Conservation Animal Monitoring / Anti-Poaching
- 4. Agriculture Vegatation Mapping / Precision Agriculture
- 5. Challenge Finding objects in Enormous Areas
- 6. Solution Combining Ground, Aerial, and Space sensors
- 8. Collect data Analyse data Earth Observation Platform Visualise data Earth Observation Platform
- 10. The idea
- 11. Artificial Intelligence Obtain Data Annotate Data Train Model Evaluate Model 1. 2. 3. 4. Supervised Learning to model the object of interest
- 12. Research Projects Onboard Animal Detection and Counting1. Object Detection in Aerial Imagery2.
- 13. Research Project 1 Nature Conservation Drones for Automatic Localization and Counting of animals J.C. van Gemert, C.R. Verschoor, P. Mettes, H.K. Epema, L.P. Koh, and S. Wich. Evaluates how object detection methods scale to drones
- 15. Dataset Data acquisition system Pelican with GoPro 1080p (1920 x 1080 px) Data acquisition process Two separate flights 4 training and 2 test videos 30 unique cows Dataset challenges Relatively small objects Skewed vantage point Data: www.camielv.nl
- 16. Detection Methods Deformable Part-Based Model (DPM) by Felzenszwalb et al. (2010) • Object is a composition of parts • Proposal score is based on root and position of the parts Colour DPM by Khan et al. (2012) • Adds colour information
- 17. Detection Methods Exemplar SVM by Malisiewicz et al. (2011) • Trains an SVM model for every exemplar in training set • Generalizes and allows knowledge sharing All methods use Histogram of Oriented Gradient features
- 18. Counting Method KLT Tracker by Kanade et al. (1981) • To obtain point tracks over time of the proposals Detections are merged using: by Everingham et al. (2009) Determines whether detections belong to the same unique animal.
- 19. Results
- 20. Results Exemplar SVM DPM Color DPM
- 21. Results
- 22. Research Project 2 Object Detection in Aerial Imagery A.E.M. Visser, J.C. van Gemert, and C.R. Verschoor (not published) An object proposal method for aerial imagery
- 23. Dataset Data acquisition system Twinstar with GoPro 7MP (3000x2250 px) Data acquisition process Various flights 577 annotated images of rhinos, zebras, people and vehicles Dataset challenges Small objects Data: www.dutchuas.nl/dataset
- 24. Proposal Method Extract descriptors • SURF descriptors by Bay et al. (2006) • Normalised RGB colour-space Train SVM Model • Classifies descriptors into objects and non-objects. Density-based clustering Filters out false positives • Density-based spatial clustering of applications with noise (DB-SCAN) • Mean shift
- 25. Proposal Method
- 26. Results
- 27. Results Pixel reduction: the percentage of pixels not in a bounding box Selective Search by Uijlings et al. (2013) Edge Boxes by Zitnick et al. (2014)
- 28. Future work Deep Learning on a Conservation Drone C. Tran, J. van Doorn, J.C. van Gemert and C.R. Verschoor (not published) Optimising Deep Learning for Nature Conservation
- 29. Future work Data Reduction Object Proposals Filter Proposals Feature Extraction Classification 1. 2. 3. 4. 5.
- 30. Thank you, be aware of the birds! CEO | camiel@dutchuas.nl |@camiel_v @dutchuasCamiel R. Verschoor