Model-based image analysis for forensic shoe print recognition

This thesis is about automated forensic shoe print recognition. Recognizing a shoe print in an image is an inherently difficult task. Shoe prints vary in their pose, shape and appearance. They are surrounded and partially occluded by other objects and may be left on a wide range of diverse surfaces. We propose to formulate this task in a model-based image analysis framework. Our framework is based on the Active Basis Model. A shoe print is represented as hierarchical composition of basis filters. The individual filters encode local information about the geometry and appearance of the shoe print pattern. The hierarchical com- position encodes mid- and long-range geometric properties of the object. A statistical distribution is imposed on the parameters of this representation, in order to account for the variation in a shoe print‘s geometry and appearance. Our work extends the Active Basis Model in various ways, in order to make it robustly applicable to the analysis of shoe print images. We propose an algorithm that automat- ically infers an efficient hierarchical dependency structure between the basis filters. The learned hierarchical dependencies are beneficial for our further extensions, while at the same time permitting an efficient optimization process. We introduce an occlusion model and propose to leverage the hierarchical dependencies to integrate contextual informa- tion efficiently into the reasoning process about occlusions. Finally, we study the effect of the basis filter on the discrimination of the object from the background. In this con- text, we highlight the role of the hierarchical model structure in terms of combining the locally ambiguous filter response into a sophisticated discriminator. The main contribution of this work is a model-based image analysis framework which represents a planar object‘s variation in shape and appearance, it‘s partial occlusion as well as background clutter. The model parameters are optimized jointly in an efficient optimization scheme. Our extensions to the Active Basis Model lead to an improved discriminative ability and permit coherent occlusions and hierarchical deformations. The experimental results demonstrate a new state of the art performance at the task of forensic shoe print recognition

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