3 research outputs found
Detection and Classification of Multiple Person Interaction
Institute of Perception, Action and BehaviourThis thesis investigates the classification of the behaviour of multiple persons when
viewed from a video camera. Work upon a constrained case of multiple person interaction
in the form of team games is investigated. A comparison between attempting
to model individual features using a (hierarchical dynamic model) and modelling the
team as a whole (using a support vector machine) is given. It is shown that for team
games such as handball it is preferable to model the whole team. In such instances
correct classification performance of over 80% are attained. A more general case of
interaction is then considered. Classification of interacting people in a surveillance
situation over several datasets is then investigated. We introduce a new feature set and
compare several methods with the previous best published method (Oliver 2000) and
demonstrate an improvement in performance. Classification rates of over 95% on real
video data sequences are demonstrated. An investigation into how the length of time a
sequence is observed is then performed. This results in an improved classifier (of over
2%) which uses a class dependent window size. The question of detecting pre/post and
actual fighting situations is then addressed. A hierarchical AdaBoost classifier is used
to demonstrate the ability to classify such situations. It is demonstrated that such an
approach can classify 91% of fighting situations correctly
Cooperative Situation Awareness in Transportation
Intelligent Transportation Systems (ITS) became a fast moving eld of research in the last decades, in particular in the context of continuously growing mobility and a high employment of resources starting from energy and material consumption to travel time and nally the human life. As it has already been experienced in other application areas, the introduction of communications technology is able to bring a revolutionary change in structures and behaviors long-believed to be carved in stone.
The main idea behind this thesis is the usage of information not as a mere placeholder, e.g. a eld in a static message, but actively utilizing its content and dependencies. This requires an estimation of the actual worth of a single piece of information for the entity itself and the entities which are in communication range. This worth has to be the essential driver for the cooperative situation estimation. The active utilization of information and its cooperative dissemination provides the entities the opportunity to become situation aware and detect hazardous or inefficient situations early in advance.
Since information always has a degree of uncertainty which is inherent to information in the real-world problem domain, as we are confronted with in ITS, probabilistic methods will be applied to model situation-relevant information. Conditional probability distributions in state transition models make for the evolvement of the situational information with the progress of time and handle causal dependencies between situational information. Together with a utility-based decision-making process dynamic probabilistic causal decision networks provide the functionality to select optimal actions given sequences of
inaccurate and incomplete evidences.
This thesis provides concepts and strategies that push forward the exploitation of information in a cooperative way within a probabilistic framework that allows to make various kinds of decisions with maximum utility. For the evaluation of the proposed concepts, the exemplary application Cooperative Adaptive Cruise Control (CACC) has been implemented on the basis of a particle lter which is used for the situation estimation. Initial simulations provided promising results and hence constitute a solid basis for future work in the eld of Cooperative Situation Awareness in Transportation
Learning multi-modal densities on discriminative temporal interaction manifold for group activity recognition
While video-based activity analysis and recognition has received much attention, existing body of work mostly deals with single object/person case. Coordinated multi-object activities, or group activities, present in a variety of applications such as surveillance, sports, and biological monitoring records, etc., are the main focus of this paper. Unlike earlier attempts which model the complex spatial temporal constraints among multiple objects with a parametric Bayesian network, we propose a Discriminative Temporal Interaction Manifold (DTIM) framework as a data-driven strategy to characterize the group motion pattern without employing specific domain knowledge. In particular, we establish probability densities on the DTIM, whose element, the discriminative temporal interaction matrix, compactly describes the coordination and interaction among multiple objects in a group activity. For each class of group activity we learn a multi-modal density function on the DTIM. A Maximum a Posteriori (MAP) classifier on the manifold is then designed for recognizing new activities. Experiments on football play recognition demonstrate the effectiveness of the approach