Report 2 Brandon Silva

Relevant Papers Multi-Target Detection and Tracking from a Single Camera in Unmanned Aerial Vehicles (UAVs) Deep Learning for Moving Object Detection and Tracking from a Single Camera in Unmanned Aerial Vehicles (UAVs) ClusterNet: Detecting Small Objects in Large Scenesby Exploiting Spatio-Temporal Information

Multi-Target Detection and Tracking from a Single Camera in Unmanned Aerial Vehicles Uses classic computer vision techniques Steps: Estimate Background Motion using Shi-Tomasi corner detector Detect Moving Objects by applying Lucas-Kanade method Classify targets and track using Kalman filter F Score Background Subtraction 0.552 Target Classification Only 0.777 Target Classification / Tracking 0.866

Deep Learning for Moving Object Detection and Tracking from a Single Camera in UAVs Uses same methods as in last paper for detection and tracking Adds a simple CNN network that takes patches of detected motion, and classifies it as moving object or false alarm This reduces false alarms and increases accuracy Detection Accuracy: Only Motion Difference Deep Learning with Appearance Precision 0.630 ± 0.11 0.819 ± 0.09 Recall 0.766 ± 0.15 0.798 ± 0.10 F-Score 0.684 ± 0.10 0.806 ± 0.08

ClusterNet Use ClusterNet network to find regions of motion, and FoveaNet network to generate heatmaps for detected motion in wide area images Uses 2D convolutional layers to preserve temporal relationship between the frames The 2 frames before and after the central frame give enough information to classify motion accurately This can can be applied to the Multi-UAV dataset, as UAVs are very small, where they cannot be seen by humans

Video Stabilization Tried a few different methods for filling in area cropped out from stabilization Uses SURF feature detector 30 Frame smoothing window is used for calculation camera motion

Challenges Stabilized frames with annotations are slightly out of sync with ground truths for the videos Needs to be fixed Calculating correct bounding boxes may also be why they are off

ClusterNet on WPAFAB Results

Next Week Fix stabilization with the annotations Generate heatmaps from bounding boxes Try to apply Foveanet network to dataset using heatmaps for labels Look at SSD/RCNN/YOLO for bounding box approach Use classic computer vision algorithms to detect motion, and pass that data alongside frames into the network to help increase accuracy