automatic animal detection - uavs in environmental research conference
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Wildlife Conservation
Animal Monitoring / Anti-Poaching
Cattle Farming
Cattle Monitoring
Agriculture
Vegatation Mapping / Precision Agriculture
Challenge
Finding objects in Enormous Areas
Solution
Combining Ground, Aerial, and Space sensors
Sensors Artificial Intelligence
Earth Observation
Platform
Solution
Collect data Analyse data
EarthObservationPlatform
Visualise data
Earth Observation Platform
Drones Artificial Intelligence
Today
Drones in Environmental Research
The idea
Artificial Intelligence
Obtain Data
Annotate Data
Train Model
Evaluate Model
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Supervised Learning to model the object of interest
Research Projects
Onboard Animal Detection and Counting1.
Object Detection in Aerial Imagery2.
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
Detection Pipeline
Dataset
Data acquisition systemPelican with GoPro1080p (1920 x 1080 px)
Data acquisition processTwo separate flights4 training and 2 test videos30 unique cows
Dataset challengesRelatively small objects Skewed vantage point
Data: www.camielv.nl
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
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
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.
Results
Results
Exemplar SVM DPM Color DPM
Results
Research Project 2
Object Detection in Aerial ImageryA.E.M. Visser, J.C. van Gemert, and C.R. Verschoor (not published)
An object proposal method for aerial imagery
Dataset
Data acquisition systemTwinstar with GoPro7MP (3000x2250 px)
Data acquisition processVarious flights577 annotated images of rhinos, zebras, people and vehicles
Dataset challengesSmall objects
Data: www.dutchuas.nl/dataset
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
Proposal Method
Results
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)
Future work
Deep Learning on a Conservation DroneC. Tran, J. van Doorn, J.C. van Gemert and C.R. Verschoor (not published)
Optimising Deep Learning for Nature Conservation
Future work
Data Reduction
Object Proposals
Filter Proposals
Feature Extraction
Classification
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