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A robust and fast automatic moving object detection and tracking system isessential to characterize target object and extract spatial and temporalinformation for different functionalities including video surveillance systems,urban traffic monitoring and navigation, robotic. In this dissertation, Ipresent a collaborative Spatial Pyramid Context-aware moving object detectionand Tracking system. The proposed visual tracker is composed of one mastertracker that usually relies on visual object features and two auxiliarytrackers based on object temporal motion information that will be calleddynamically to assist master tracker. SPCT utilizes image spatial context atdifferent level to make the video tracking system resistant to occlusion,background noise and improve target localization accuracy and robustness. Wechose a pre-selected seven-channel complementary features including RGB color,intensity and spatial pyramid of HoG to encode object color, shape and spatiallayout information. We exploit integral histogram as building block to meet thedemands of real-time performance. A novel fast algorithm is presented toaccurately evaluate spatially weighted local histograms in constant timecomplexity using an extension of the integral histogram method. Differenttechniques are explored to efficiently compute integral histogram on GPUarchitecture and applied for fast spatio-temporal median computations and 3Dface reconstruction texturing. We proposed a multi-component framework based onsemantic fusion of motion information with projected building footprint map tosignificantly reduce the false alarm rate in urban scenes with many tallstructures. The experiments on extensive VOTC2016 benchmark dataset and aerialvideo confirm that combining complementary tracking cues in an intelligentfusion framework enables persistent tracking for Full Motion Video and WideAerial Motion Imagery.

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