Pedestrian tracking using probability fields and a movement feature space

Pedestrian tracking using probability fields and a movement feature space 1

Retrieving useful information from video sequences, such as the dynamics of pedestrians, and other moving objects on a video sequence, leads to further knowledge of what is happening on a scene. In this paper, a Target Framework associates each person with an autonomous entity, modeling its trajectory and speed by using a state machine. The particularity of our methodology is the use of a Movem...

Pedestrian detection on CAVIAR dataset using a movement feature space

This work develops a pedestrian detection system using a feature space based on level lines, called Movement Feature Space (MFS). Besides detecting the movement in the scene, this feature space defines the descriptors used by the classifiers to identify pedestrians. Locations hypotheses of pedestrian are performed by a cascade of boosted classifiers. The validation of these regions of interest ...

Feature Inference: Tracking Mouse Movement

Past research suggests inductive judgments are made via simply assessing feature similarity (Osherson et al, 1990) while other research (Gelman & Markman, 1986) proposed that category labels convey information beyond other features. To further investigate these claims, we developed an online measure of decision-making. The present study examines how category labels affect inductive inferences b...

Memory-guided tracking through physical space and feature space.

People can estimate the current position of an occluded moving target. This is called motion extrapolation, and it has been suggested that the performance in such tasks is mediated by the smooth-pursuit system. Experiment 1 contrasted a standard position extrapolation task with a novel number extrapolation task. In the position extrapolation task, participants saw a horizontally moving target b...

Tracking of human movement in image space