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.

Visual Object Recognition Using Deformable Models of Vehicles

This workshop paper reports recent developments to a vision system for traffic interpretation which relies extensively on the use of geometrical and scene context. Firstly, a new approach to pose refinement is reported, based on forces derived from prominent image derivatives found close to an initial hypothesis. Secondly, a parameterised vehicle model is reported, able to represent different vehicle classes. This general vehicle model has been fitted to sample data, and subjected to a Principal Component Analysis to create a deformable model of common car types having 6 parameters. We show that the new pose recovery technique is also able to operate on the PCA model, to allow the structure of an initial vehicle hypothesis to be adapted to fit the prevailing context. We report initial experiments with the model, which demonstrate significant improvements to pose recovery.

Pose and Structure Recovery using Active Models

A new formulation of a pose refinement technique using ``active'' models is described. An error term derived from the detection of image derivatives close to an initial object hypothesis is linearised and solved by least squares. The method is particularly well suited to problems involving external geometrical constraints (such as the ground-plane constraint). We show that the method is able to recover both the pose of a rigid model, and the structure of a deformable model. We report an initial assessment of the performance and cost of pose and structure recovery using the active model in comparison with our previously reported ``passive'' model-based techniques in the context of traffic surveillance. The new method is more stable, and requires fewer iterations, especially when the number of free parameters increases, but shows somewhat poorer convergence.

Opportunities for improvements in flocking models

Flocking is the capacity of coherent movement between multiple animals, including birds. Prominent research into flocking is presented. Particle Swarm Optimisation (PSO) has been the prominent result from research into flocking. It is considered that opportunities for further research in flocking exist. With the potential for automated traffic systems, it is concluded that flocking should be reinvestigated for this purpose.

The impact of traffic emissions on atmospheric ozone and OH: results from QUANTIFY

To estimate the impact of emissions by road, aircraft and ship traffic on ozone and OH in the present-day atmosphere six different atmospheric chemistry models have been used. Based on newly developed global emission inventories for road, ship and aircraft emission data sets each model performed sensitivity simulations reducing the emissions of each transport sector by 5%. The model results indicate that on global annual average lower tropospheric ozone responds most sensitive to ship emissions (50.6%±10.9% of the total traffic induced perturbation), followed by road (36.7%±9.3%) and aircraft exhausts (12.7%±2.9%), respectively. In the northern upper troposphere between 200–300 hPa at 30–60° N the maximum impact from road and ship are 93% and 73% of the maximum effect of aircraft, respectively. The latter is 0.185 ppbv for ozone (for the 5% case) or 3.69 ppbv when scaling to 100%. On the global average the impact of road even dominates in the UTLS-region. The sensitivity of ozone formation per NOx molecule emitted is highest for aircraft exhausts. The local maximum effect of the summed traffic emissions on the ozone column predicted by the models is 0.2 DU and occurs over the northern subtropical Atlantic extending to central Europe. Below 800 hPa both ozone and OH respond most sensitively to ship emissions in the marine lower troposphere over the Atlantic. Based on the 5% perturbation the effect on ozone can exceed 0.6% close to the marine surface (global zonal mean) which is 80% of the total traffic induced ozone perturbation. In the southern hemisphere ship emissions contribute relatively strongly to the total ozone perturbation by 60%–80% throughout the year. Methane lifetime changes against OH are affected strongest by ship emissions up to 0.21 (± 0.05)%, followed by road (0.08 (±0.01)%) and air traffic (0.05 (± 0.02)%). Based on the full scale ozone and methane perturbations positive radiative forcings were calculated for road emissions (7.3±6.2 mWm−2) and for aviation (2.9±2.3 mWm−2). Ship induced methane lifetime changes dominate over the ozone forcing and therefore lead to a net negative forcing (−25.5±13.2 mWm−2).

Learning enhanced 3D models for vehicle tracking

This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic object-centred and appearance-based representations in computer vision giving improved hypothesis verification under iconic matching. The "appearance" of a 3D object is learnt using an eigenspace representation obtained as it is tracked through a scene. The feature representation implicitly models the background and the objects observed enabling the segmentation of the objects from the background. The method is shown to enhance model-based tracking, particularly in the presence of clutter and occlusion, and to provide a basis for identification. The unified approach is discussed in the context of the traffic surveillance domain. The approach is demonstrated on real-world image sequences and compared to previous (edge-based) iconic evaluation techniques.

Visual surveillance using deformable models of vehicles

This paper presents recent developments to a vision-based traffic surveillance system which relies extensively on the use of geometrical and scene context. Firstly, a highly parametrised 3-D model is reported, able to adopt the shape of a wide variety of different classes of vehicle (e.g. cars, vans, buses etc.), and its subsequent specialisation to a generic car class which accounts for commonly encountered types of car (including saloon, batchback and estate cars). Sample data collected from video images, by means of an interactive tool, have been subjected to principal component analysis (PCA) to define a deformable model having 6 degrees of freedom. Secondly, a new pose refinement technique using “active” models is described, able to recover both the pose of a rigid object, and the structure of a deformable model; an assessment of its performance is examined in comparison with previously reported “passive” model-based techniques in the context of traffic surveillance. The new method is more stable, and requires fewer iterations, especially when the number of free parameters increases, but shows somewhat poorer convergence. Typical applications for this work include robot surveillance and navigation tasks.