Geoffrey D. Sullivan

Model-Based Localisation and Recognition of Road Vehicles

Objects are often constrained to lie on a known plane. This paper concerns the pose determination and recognition of vehicles in traffic scenes, which under normal conditions stand on the ground-plane. The ground-plane constraint reduces the problem of localisation and recognition from 6 dof to 3 dof.The ground-plane constraint significantly reduces the pose redundancy of 2D image and 3D model line matches. A form of the generalised Hough transform is used in conjuction with explicit probability-based voting models to find consistent matches and to identify the approximate poses. The algorithms are applied to images of several outdoor traffic scenes and successful results are obtained. The work reported in this paper illustrates the efficiency and robustness of context-based vision in a practical application of computer vision.Multiple cameras may be used to overcome the limitations of a single camera. Data fusion in the proposed algorithms is shown to be simple and straightforward.

A generic deformable model for vehicle recognition

This paper reports the development of a highly parameterised 3-D model able to adopt the shapes of a wide variety of different classes of vehicles (cars, vans, buses, etc), and its subsequent specialisation to a generic car class which accounts for most commonly encountered types of car (includng saloon, hatchback and estate cars). An interactive tool has been developed to obtain sample data for vehicles from video images. A PCA description of the manually sampled data provides a deformable model in which a single instance is described as a 6 parameter vector. Both the pose and the structure of a car can be recovered by fitting the PCA model to an image. The recovered description is sufficiently accurate to discriminate between vehicle sub-classes.

A simple, intuitive camera calibration tool for natural images

The paper reports an interactive tool for calibrating a camera, suitable for use in outdoor scenes. The motivation for the tool was the need to obtain an approximate calibration for images taken with no explicit calibration data. Such images are frequently presented to research laboratories, especially in surveillance applications, with a request to demonstrate algorithms. The method decomposes the calibration parameters into intuitively simple components, and relies on the operator interactively adjusting the parameter settings to achieve a visually acceptable agreement between a rectilinear calibration model and his own perception of the scene. Using the tool, we have been able to calibrate images of unknown scenes, taken with unknown cameras, in a matter of minutes. The standard of calibration has proved to be sufficient for model-based pose recovery and tracking of vehicles.

Recognizing objects on the ground-plane

Abstract Objects such as vehicles are often constrained to lie on a known plane. The ground-plane constraint reduces the problem of localization and recognition from 6 to 3 DOF. A novel algorithm is presented which makes effective use of the ground-plane constraint to derive pose estimates. A form of the generalized Hough transform is used to group evidence from line features, and to identify approximate poses. The single orientation parameter is decoupled from the two location parameters, and dealt with separately. The method is fast and robust. It copes well with complex outdoor scenes including multiple occluded objects, and image clutter from irrelevant structures.

Model bases perspective inversion

Abstract The problem of finding the spatial correspondence between an object and the image of the object under perspective projection is investigated and a new technique is demonstrated. This technique is based on a geometrical description, or model, of the object and a least squares solution of the resulting nonlinear equations. An analysis of performance and a comparison with Lowes previous work is given. Three further areas of applications in model based vision are discussed.

The automatic construction of a view-independent relational model for 3-D object recognition

A view-independent relational model (VIRM) used in a vision system for recognizing known 3-D objects from single monochromatic images of unknown scenes is described. The system inspects a CAD model from a number of different viewpoints, and a statistical interference is applied to identify relatively view-independent relationships among component parts of the object. These relations are stored as a relational model of the object, which is represented in the form of a hypergraph. Three-dimensional components of the object, which can be associated with extended image features obtained by grouping of primitive 2-D features are represented as nodes of the hypergraph. Covisibility of model features is represented by means of hyperedges of the hypergraph, and the pairwise view-independent relations form procedural constraints associated with the hypergraph edges. During the recognition phase, the covisibility measures allow a best-first search of the graph for acceptable matches. >

Model-Based Tracking.

Model-based vision techniques originally developed for the recognition and pose recovery of a vehicle in a single image, are used here to track a vehicle through a sequence of images. The knowledge of the position of the camera with respect to the ground plane is used to reduce the search space of possible vehicle positions from six dimensions to three.

Pose determination and recognition of vehicles in traffic scenes

This paper concerns the pose determination and recognition of vehicles in traffic scenes, which under normal conditions stand on the ground-plane. Novel linear and closed-form algorithms are described for pose determination from an arbitrary number of known line matches. A form of the generalised Hough transform is used in conjuction with explicit probability-based voting models to find consistent matches. The algorithms are fast and robust. They cope well with complex outdoor scenes.

Pose refinement of active models using forces in 3D

A new algorithm is described for refining the pose of a model of a rigid object, to conform more accurately to the image structure. Elemental 3D forces are considered to act on the model. These are derived from directional derivatives of the image local to the projected model features. The convergence properties of the algorithm is investigated and compared to a previous technique. Its use in a video sequence of a cluttered outdoor traffic scene is also illustrated and assessed.

Filter for Car Tracking Based on Acceleration and Steering Angle

The motion of a car is described using a stochastic model in which the driving processes are the steering angle and the tangential acceleration. The model incorporates exactly the kinematic constraint that the wheels do not slip sideways. Two filters based on this model have been implemented, namely the standard EKF, and a new filter (the CUF) in which the expectation and the covariance of the system state are propagated accurately. Experiments show that i) the CUF is better than the EKF at predicting future positions of the car; and ii) the filter outputs can be used to control the measurement process, leading to improved ability to recover from errors in predictive tracking.