https://personalpages.manchester.ac.uk/staff/timothy.f.cootes/Papers/cviu95.pdf

Active shape models—their training and application

The goal of this article is to review the state-of-the-art tracking methods, classify them into different categories, and identify new trends. Object tracking, in general, is a challenging problem. Difficulties in tracking objects can arise due to abrupt object motion, changing appearance patterns of both the object and the scene, nonrigid object structures, object-to-object and object-to-scene occlusions, and camera motion. Tracking is usually performed in the context of higher-level applications that require the location and/or shape of the object in every frame. Typically, assumptions are made to constrain the tracking problem in the context of a particular application. In this survey, we categorize the tracking methods on the basis of the object and motion representations used, provide detailed descriptions of representative methods in each category, and examine their pros and cons. Moreover, we discuss the important issues related to tracking including the use of appropriate image features, selection of motion models, and detection of objects.

/pdf/object-tracking-a-survey-3706dclxb5.pdf

Object tracking: A survey

This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction.

Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast.

Details of the new feature detectors and of the new noise reduction method are described, along with test results.

SUSAN—A New Approach to Low Level Image Processing

Visual surveillance in dynamic scenes, especially for humans and vehicles, is currently one of the most active research topics in computer vision. It has a wide spectrum of promising applications, including access control in special areas, human identification at a distance, crowd flux statistics and congestion analysis, detection of anomalous behaviors, and interactive surveillance using multiple cameras, etc. In general, the processing framework of visual surveillance in dynamic scenes includes the following stages: modeling of environments, detection of motion, classification of moving objects, tracking, understanding and description of behaviors, human identification, and fusion of data from multiple cameras. We review recent developments and general strategies of all these stages. Finally, we analyze possible research directions, e.g., occlusion handling, a combination of twoand three-dimensional tracking, a combination of motion analysis and biometrics, anomaly detection and behavior prediction, content-based retrieval of surveillance videos, behavior understanding and natural language description, fusion of information from multiple sensors, and remote surveillance.

/pdf/a-survey-on-visual-surveillance-of-object-motion-and-302zsaqo09.pdf

A survey on visual surveillance of object motion and behaviors

A Survey of Computer Vision-Based Human Motion Capture

Personal identification based on handwriting

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.

Model-Based Localisation and Recognition of Road Vehicles

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

http://www.bmva.org/bmvc/1995/bmvc-95-012.pdf

A generic deformable model for vehicle recognition

This paper reports the current state of work to simplify our previous model-based methods for visual tracking of vehicles for use in a realtime system intended to provide continuous monitoring and classification of traffic from a fixed camera on a busy multi-lane motorway. The main constraints of the system design were: (i) all low level processing is to be carried out by low-cost auxiliary hardware; (ii) all 3-D reasoning is to be carried out automatically off-line, at set-up time. The system developed uses three main stages: (i) pose and model hypothesis using 1-D templates, (ii) hypothesis tracking, and (iii) hypothesis verification, using 2-D templates. Stages (i) and (iii) have radically different computing performance and computational costs. and need to be carefully balanced for efficiency. Together, they provide an effective way to locate, track and classify vehicles.

Model-based vehicle detection and classification using orthographic approximations

The paper describes a novel integrated vision system in which two autonomous visual modules are combined to interpret a dynamic scene. The first module employs a 3D model-based scheme to track rigid objects such as vehicles. The second module uses a 2D deformable model to track non-rigid objects such as people. The principal contribution is a novel method for handling occlusion between objects within the context of this hybrid tracking system. The practical aim of the work is to derive a scene description that is sufficiently rich to be used in a range of surveillance tasks. The paper describes each of the modules in outline before detailing the method of integration and the handling of occlusion in particular. Experimental results are presented to illustrate the performance of the system in a dynamic outdoor scene involving cars and people.

Keith D. Baker

Papers

Personal identification based on handwriting

Model-Based Localisation and Recognition of Road Vehicles

A generic deformable model for vehicle recognition

Model-based vehicle detection and classification using orthographic approximations

An integrated traffic and pedestrian model-based vision system