This paper demonstrates a new visual motion estimation technique that is able to recover high degree-of-freedom articulated human body configurations in complex video sequences. We introduce the use of a novel mathematical technique, the product of exponential maps and twist motions, and its integration into a differential motion estimation. This results in solving simple linear systems, and en...

In this paper we present a combined probability estimation approach to detect and track multiple people for pose estimation at the same time. It can deal with partial and total occlusion between persons by adding torso appearance to the tracker. Moreover, the upper body of each individual is further segmented into head, torso, upper arm and lower arm in a hierarchical way. The simplicity of the...

The Cluster Tracker, introduced in previous work, is used to detect, track, split, merge and remove clusters of pixels significantly different from the corresponding pixels in a reference image. Clusters with common motion are grouped together into super-clusters during off-line processing, and the number of people in each super-cluster is determined by the sizes of the super-clusters and their...

People tracking is an important yet challenging task for mobile robots operating in populated environments and interacting with humans. What makes this problem difficult is that human behavior is complex and hard to predict. However, motion of people, the rate at which people appear and where they appear are not random but strongly place-dependent and follow patterns that are engendered by the ...

An important building block for intelligent mobile robots is the ability to track people moving around in the environment. Algorithms for person-tracking often incorporate motion models, which can improve tracking accuracy by predicting how people will move. More accurate motion models produce better tracking because they allow us to average together multiple predictions of the person’s locatio...

Real applications on people tracking are usually based on image heuristics. Real approaches do not use to apply recent prediction-estimation theoretical frameworks. These require the definition of complex dynamical and shape object models before the tracking process. We present a probabilistic framework that takes profit of these theories adapting them to real applications. The key idea of this...

Re-identification is usually defined as the problem of deciding whether a person currently in the field of view of a camera has been seen earlier either by that camera or another. However, a different version of the problem arises even when people are seen by multiple cameras with overlapping fields of view. Current tracking algorithms can easily get confused when people come close to each othe...

This work proposes a novel filtering algorithm that constitutes an extension of Bayesian particle filters to the Dempster–Shafer theory. Our proposal solves the multi-target problem by combining evidences from multiple heterogeneous and unreliable sensors. The modelling of uncertainty and absence of knowledge in our approach is specially attractive since it does not require to specify prior nor...

This work presents a novel people tracking approach, able to cope with frequent shape changes and large occlusions. In particular, the tracks are described by means of probabilistic masks and appearance models. Occlusions due to other tracks, or due to background objects and false occlusions are discriminated. The classification is exploited in a selective model update. The tracking system is g...

Multi-target tracking (MTT) is an active and challenging research topic. Many different approaches to MTT problem exist, yet there are still few satisfactory methods of solving multi-target occlusion problem, which often appears in multi-target tracking task. The application of multi cameras in most existing researches for multi-target occlusion requires camera calibration parameters in advance...