A probabilistic framework for tracking in wide-area environments

Surveillance in wide-area spatial environments is characterised by complex spatial layouts, large state space, and the use of multiple cameras/sensors. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. This requirement is particularly suited to the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN). In this paper, we propose the use of LDPN as the integrated framework for tracking in wide-area environments. We illustrate, with the help of a synthetic tracking scenario, how the parameters of the LDPN can be estimated from training data, and then used to draw predictions and answer queries about unseen tracks at various levels of detail.<br /

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.<br /

On the automated interpretation and indexing of American football

This work combines natural language understanding and image processing with incremental learning to develop a system that can automatically interpret and index American Football. We have developed a model for representing spatio-temporal characteristics of multiple objects in dynamic scenes in this domain. Our representation combines expert knowledge, domain knowledge, spatial knowledge and temporal knowledge. We also present an incremental learning algorithm to improve the knowledge base as well as to keep previously developed concepts consistent with new data. The advantages of the incremental learning algorithm are that is that it does not split concepts and it generates a compact conceptual hierarchy which does not store instances

Robust recognition and segmentation of human actions using HMMs with missing observations

This paper describes the integration of missing observation data with hidden Markov models to create a framework that is able to segment and classify individual actions from a stream of human motion using an incomplete 3D human pose estimation. Based on this framework, a model is trained to automatically segment and classify an activity sequence into its constituent subactions during inferencing. This is achieved by introducing action labels into the observation vector and setting these labels as missing data during inferencing, thus forcing the system to infer the probability of each action label. Additionally, missing data provides recognition-level support for occlusions and imperfect silhouette segmentation, permitting the use of a fast (real-time) pose estimation that delegates the burden of handling undetected limbs onto the action recognition system. Findings show that the use of missing data to segment activities is an accurate and elegant approach. Furthermore, action recognition can be accurate even when almost half of the pose feature data is missing due to occlusions, since not all of the pose data is important all of the time

Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification

Logistic regression is well known to the data mining research community as a tool for modeling and classification. The presence of outliers is an unavoidable phenomenon in data analysis. Detection of outliers is important to increase the accuracy of the required estimates and for reliable knowledge discovery from the underlying databases. Most of the existing outlier detection methods in regression analysis are based on the single case deletion approach that is inefficient in the presence of multiple outliers because of the well known masking and swamping effects. To avoid these effects the multiple case deletion approach has been introduced. We propose a group deletion approach based diagnostic measure for identifying multiple influential observations in logistic regression. At the same time we introduce a plotting technique that can classify data into outliers, high leverage points, as well as influential and regular observations. This paper has two objectives. First, it investigates the problems of outlier detection in logistic regression, proposes a new method that can find multiple influential observations, and classifies the types of outlier. Secondly, it shows the necessity for proper identification of outliers and influential observations as a prelude for reliable knowledge discovery from modeling and classification via logistic regression. We demonstrate the efficiency of our method, compare the performance with the existing popular diagnostic methods, and explore the necessity of outlier detection for reliability and robustness in modeling and classification by using real datasets

Low-cost interactive active monocular range finder

This paper describes a low-cost interactive active monocular range finder and illustrates the effect of introducing interactivity to the range acquisition process. The range finder consists of only one camera and a laser pointer, to which three LEDs are attached. When a user scans the laser along surfaces of objects, the camera captures the image of spots (one from the laser, and the others from LEDs), and triangulation is carried out using the camera\u27s viewing direction and the optical axis of the laser. The user interaction allows the range finder to acquire range data in which the sampling rate varies across the object depending on the underlying surface structures. Moreover, the processes of separating objects from the background and/or finding parts in the object can be achieved using the operator\u27s knowledge of the objects

Temporal PDMs for gait classification

Gait classification is a developing research area, particularly with regards to biometrics. It aims to use the distinctive spatial and temporal characteristics of human motion to classify differing activities. As a biometric, this extends to recognising different people by the heterogeneous aspects of their gait. This research aims to use a modified deformable model, the temporal PDM, to distinguish the movements of a walking and miming person. The movement of 2D points on the moving form is used to provide input into the model and classify the type of gait present.<br /

Combining image regions and human activity for indirect object recognition in indoor wide-angle views

Traditional methods of object recognition are reliant on shape and so are very difficult to apply in cluttered, wideangle and low-detail views such as surveillance scenes. To address this, a method of indirect object recognition is proposed, where human activity is used to infer both the location and identity of objects. No shape analysis is necessary. The concept is dubbed \u27interaction signatures\u27, since the premise is that a human will interact with objects in ways characteristic of the function of that object - for example, a person sits in a chair and drinks from a cup. The human-centred approach means that recognition is possible in low-detail views and is largely invariant to the shape of objects within the same functional class. This paper implements a Bayesian network for classifying region patches with object labels, building upon our previous work in automatically segmenting and recognising a human\u27s interactions with the objects. Experiments show that interaction signatures can successfully find and label objects in low-detail views and are equally effective at recognising test objects that differ markedly in appearance from the training objects.<br /

A framework for the design of privacy preserving pervasive healthcare

Privacy is an important aspect of pervasive and ubiquitous computing systems, and, in particular, pervasive healthcare. With reference to previous approaches on developing privacy sensitive pervasive healthcare applications, we detail a framework for the design of such systems that aims to minimise the impact of privacy on such systems. In reviewing previous approaches, we extract and combine common elements in order to unify the approaches and create a more formal methodology for designing privacy mechanisms in pervasive healthcare applications. In doing so we also consider the manner in which ubiquitous technologies impact on privacy and methods for reducing this impact. We demonstrate how the framework can be applied by using examples from the previous approaches. In addressing privacy issues, the framework aims to remove a large obstacle to deployment of pervasive healthcare systems, acceptance of the technology.<br /

Emergent intertransaction association rules for abnormality detection in intelligent environments

This paper is concerned with identifying anomalous behaviour of people in smart environments. We propose the use of emergent transaction mining and the use of the extended frequent pattern tree as a basis. Our experiments on two data sets demonstrate that emergent intertransaction associations are able to detect abnormality present in real world data and that both short and long term behavioural changes can be discovered. The use of intertransaction associations is shown to be advantageous in the detection of temporal associationanomalies otherwise not readily detectable by traditional "market basket" intratransaction mining