![Loading...](https://link.springer.com/static/c4a417b97a76cc2980e3c25e2271af3129e08bbe/images/pdf-preview/spacer.gif)
-
Chapter and Conference Paper
Automatic Learning of Background Semantics in Generic Surveilled Scenes
Advanced surveillance systems for behavior recognition in outdoor traffic scenes depend strongly on the particular configuration of the scenario. Scene-independent trajectory analysis techniques statistically ...
-
Chapter and Conference Paper
Autonomous Virtual Agents for Performance Evaluation of Tracking Algorithms
This paper describes a framework which exploits the use of computer animation to evaluate the performance of tracking algorithms. This can be achieved in two different, complementary strategies. On the one han...
-
Chapter and Conference Paper
Automatic Learning of Conceptual Knowledge in Image Sequences for Human Behavior Interpretation
This work describes an approach for the interpretation and explanation of human behavior in image sequences, within the context of a Cognitive Vision System. The information source is the geometrical data obtaine...
-
Chapter and Conference Paper
3D Human Motion Sequences Synchronization Using Dense Matching Algorithm
This work solves the problem of synchronizing pre-recorded human motion sequences, which show different speeds and accelerations, by using a novel dense matching algorithm. The approach is based on the dynamic...
-
Chapter and Conference Paper
Posture Constraints for Bayesian Human Motion Tracking
One of the most used techniques for full-body human tracking consists of estimating the probability of the parameters of a human body model over time by means of a particle filter. However, given the high-dime...
-
Chapter and Conference Paper
A 3D Dynamic Model of Human Actions for Probabilistic Image Tracking
In this paper we present a method suitable to be used for human tracking as a temporal prior in a particle filtering framework such as CONDENSATION [5]. This method is for predicting feasible human postures given...
-
Chapter and Conference Paper
Probabilistic Image-Based Tracking: Improving Particle Filtering
Condensation is a widely-used tracking algorithm based on particle filters. Although some results have been achieved, it has several unpleasant behaviours. In this paper, we highlight the...