Real Time Fusion of Radioisotope Direction Estimation and Visual Object Tracking

Research into discovering prohibited nuclear material plays an integral role in providing security from terrorism. Although many diverse methods contribute to defense, there exists a capability gap in localizing moving sources. This thesis introduces a real time radioisotope tracking algorithm assisted by visual object tracking methods to fill the capability gap. The proposed algorithm can estimate carrier likelihood for objects in its field of view, and is designed to assist a pedestrian agent wearing a backpack detector. The complex, crowd-filled, urban environments where this algorithm must function combined with the size and weight limitations of a pedestrian system makes designing a functioning algorithm challenging.The contribution of this thesis is threefold. First, a generalized directional estimator is proposed. Second, two state-of-the-art visual object detection and visual object tracking methods are combined into a single tracking algorithm. Third, those outputs are fused to produce a real time radioisotope tracking algorithm. This algorithm is designed for use with the backpack detector built by the IDEAS for WIND research group. This setup takes advantage of recent advances in detector, camera, and computer technologies to meet the challenging physical limitations.The directional estimator operates via gradient boosting regression to predict radioisotope direction with a variance of 50 degrees when trained on a simple laboratory dataset. Under conditions similar to other state-of-the-art methods, the accuracy is comparable. YOLOv3 and SiamFC are chosen by evaluating advanced visual tracking methods in terms of speed and efficiency across multiple architectures, and in terms of accuracy on datasets like the Visual Object Tracking (VOT) Challenge and Common Objects in Context (COCO). The resultant tracking algorithm operates in real time. The outputs of direction estimation and visual tracking are fused using sequential Bayesian inference to predict carrier likelihood. Using lab trials evaluated by hand on visual and nuclear data, and a synthesized challenge dataset using visual data from the Boston Marathon attack, it can be observed that this prototype system advances the state-of-the-art towards localization of a moving source

Spatial Pyramid Context-Aware Moving Object Detection and Tracking for Full Motion Video and Wide Aerial Motion Imagery

A robust and fast automatic moving object detection and tracking system is essential to characterize target object and extract spatial and temporal information for different functionalities including video surveillance systems, urban traffic monitoring and navigation, robotic. In this dissertation, I present a collaborative Spatial Pyramid Context-aware moving object detection and Tracking system. The proposed visual tracker is composed of one master tracker that usually relies on visual object features and two auxiliary trackers based on object temporal motion information that will be called dynamically to assist master tracker. SPCT utilizes image spatial context at different level to make the video tracking system resistant to occlusion, background noise and improve target localization accuracy and robustness. We chose a pre-selected seven-channel complementary features including RGB color, intensity and spatial pyramid of HoG to encode object color, shape and spatial layout information. We exploit integral histogram as building block to meet the demands of real-time performance. A novel fast algorithm is presented to accurately evaluate spatially weighted local histograms in constant time complexity using an extension of the integral histogram method. Different techniques are explored to efficiently compute integral histogram on GPU architecture and applied for fast spatio-temporal median computations and 3D face reconstruction texturing. We proposed a multi-component framework based on semantic fusion of motion information with projected building footprint map to significantly reduce the false alarm rate in urban scenes with many tall structures. The experiments on extensive VOTC2016 benchmark dataset and aerial video confirm that combining complementary tracking cues in an intelligent fusion framework enables persistent tracking for Full Motion Video and Wide Aerial Motion Imagery.Comment: PhD Dissertation (162 pages