Long Range Automated Persistent Surveillance

This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera’s field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera’s zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target’s 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels. Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images

Qualitative Multi-Scale Feature Hierarchies for Object Tracking

This paper shows how the performance of feature trackers can be improved by building a hierarchical view-based object representation consisting of qualitative relations between image structures at di#erent scales. The idea is to track all image features individually, and to use the qualitative feature relations for avoiding mismatches, resolving ambiguous matches and for introducing feature hypotheses whenever image features are lost. Compared to more traditional work on view-based object tracking, this methodology has the ability to handle semi-rigid objects and partial occlusions. Compared to trackers based on threedimensional object models, this approach is much simpler and of a more generic nature. A hands-on example is presented showing how an integrated application system can be constructed from conceptually very simple operations

Template reduction of feature point models for rigid objects and application to tracking in microscope images.

This thesis addresses the problem of tracking rigid objects in video sequences. A novel approach to reducing the template size of shapes is presented. The reduced shape template can be used to enhance the performance of tracking, detection and recognition algorithms. The main idea consists of pre-calculating all possible positions and orientations that a shape can undergo for a given state space. From these states, it is possible to extract a set of points that uniquely and robustly characterises the shape for the considered state space. An algorithm, based on the Hough transform, has been developed to achieve this for discrete shapes, i.e. sets of points, projected in an image when the state space is bounded. An extended discussion on particle filters, that serves as an introduction to the topic, is presented, as well as some generic improvements. The introduction of these improvements allow the data to be better sampled by incorporating additional measurements and knowledge about the velocity of the tracked object. A partial re-initialisation scheme is also presented that enables faster recovery of the system when the object is temporarily occluded.A stencil estimator is introduced to identify the position of an object in an image. Some of its properties are discussed and demonstrated. The estimator can be efficiently evaluated using the bounded Hough transform algorithm. The performance of the stencilled Hough transform can be further enhanced with a methodology that decimates the stencils while maintaining the robustness of the tracker. Performance evaluations have demonstrated the relevance of the approach. Although the methods presented in this thesis could be adapted to full 3-D object motion, motions that maintain the same view of the object in front of a camera are more specifically studied