Moving Target Detection Using Infrared Sensors Arisa Kitagishi, Babak Ebrahimi, Dr. Abhijit Mahalanobis University of Central Florida Introduction Methods Results To provide better object detection using infrared sensors as not many computer vision research focus on frames obtained from infrared sensors. We would like to provide fast and accurate predictions using our network to be able to utilize it in real-life situations.  Our goal is to achieve 80% or more in recall with less than 0.1 false alarm rate at long ranges (more than 4km). We initially had background subtraction alone to see the results FoveaNet Then, we applied CNN to it to improve the results                                 Faster RCNN performed about 60% lower recall at false alarm rate of 1.1 compared to our method. Faster RCNN tends to have high false alarm rate when targets were detected.  Without CNN Without CNN Background Subtraction Results/ Experiments Dataset Data                         Ground Truth                                Prediction We plotted the results using constant false  alarm rate to eliminate outlier caused by the low threshold  NVESDA ATR Development Dataset: Taken using infrared sensors Contains 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, and 5km ranges Contains night and day time folders Each video contains 1800 frames What we utilized: 2 furthest ranges (4.5km and 5km) Night and Day folders 34 videos The recall increased more than 15% at false alarm rate of 0.5 and even more for smaller false alarm rates by incorporating the CNN in the process.  With CNN Faster RCNN CNN References Background Subtraction 1. Rodney LaLonde, Dong Zhang, and Mubarak Shah, ClusterNet: Detecting Small Objects in Large Scenes by Exploiting Spatio-Temporal Information, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018), Salt Lake City, UT, June 18-22, 2018.