1. Week 6: Moving Target Detection Using Infrared Sensors
Project Leaders: Dr. Mahalanobis and Babak
REU Student: Arisa Kitagishi

2. Overview Overview of Our Research Tasks and Achievements
Current State of Our Research

3. Overview of Our Research
Goal : Detect targets at long ranges (> 4km)
Approach:
Implement any CNN architecture
Train and test the CNN
Characterize performance
Achieve recall > 80% with false alarm rate < 0.1 at distances > 4km

4. Datasets and GT
NVESDA ATR Development Dataset

5. Datasets and GT

6. Training Models There are two different types of models:
Models with small amount of training size
Models with proper amount (80% of dataset) of training size
All has learning rate of with MSE Loss, 20 epochs, night and day, frames per videos, for the furthest range (5km) only

7. Testing Models: 1. Models with Small Amount of Training Size
Prediction GT Data

8. Testing Models: 2. Models with proper amount (80%) of training size
Prediction GT Data

9. Changing ROC and Current Best Result
Changed the method of counting targets from counting connected components to constant false alarm rate
Original CFAR

10. Background Subtraction
Furthest Range: 5km Ranges: 4.5km and 5km

11. Current State CNN
Thresholds: [0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95]
Recalls: [0.9321, , 0.932, 0.918, 0.908, 0.901, 0.894, 0.886, , , , , , , , , , , ]
Average false alarms per frame: [ , , , , , 0.11, , , , , , , , 0.01, , , , , ]

12. Plans
Current Plan:
Faster RCNN
Further Plan:
YOLO

13. Thank you! ☺️