1. Person Detection in Drone
Imagery
SAŠA SAMBOLEK
sasa.sambolek@gmail.com
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2. Agenda
1. Why drone imagery
2. Main challenges
3. Computer vision
4. Drones datasets
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3. Why drone imagery
Motivation: Search and rescue – non-urban areas
Time is a key factor
A large human potential and material resources
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4. Why drone imagery
Croatian Mountain Rescue Service - Unmanned Systems Department
Currently: Search during the flight + "manual" analysis of recordings
Goal: using deep neural networks to perform person detection on drone footage in non-urban
areas.
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5. Main challenges
1. Adverse environmental conditions
2. Limited data availability
3. Complex terrain and structures
4. Real-time processing
5. Cost and availability of equipment
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6. Computer vision
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7. Basic Computer Vision tasks
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8. CNN convolutional nural network
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9. CNN – detectors
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11. Drones datasets
Existing data sets of images recorded from unmanned aerial vehicles - drones
• Campus
• CARPK
• UAV123
• Ocutama-Action
• VisDrone
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12. SARD dataset
DJI Phantom 4 Advanced
8 – video recordings in resolution
1920x1080 px, 50fps
5 – 50 m (AGL)
Actors from 7 to 55 years of age
1981 images
(6532 manually marked persons)
LabelImg
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18. SARDCorr
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19. SARD - statistics
Number of images: 1981
Number of objects: 6532
Small objects : 1883 ( area < 322 )
Medium objects: 4180 ( 322 < area < 962 )
Large objects: 469 ( 962 < area )
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20. ROpti
• FP = 0, ROpti = Recall
• FP = 0 i FN = 0, ROpti = 1
• TP = FP, ROpti = 0
𝑅𝑂𝑝𝑡𝑖 =
𝑇𝑃 − 𝐹𝑃
𝑇𝑃 + 𝐹𝑁

21. Transfer learning

22. Instead of Conclusions

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Saša Sambolek
sasa.sambolek@gmail.com
Thank you for attention