Abstract not found

Everyday tasks often require us to keep track of multiple objects in dynamic scenes. Past studies show that tracking becomes more difficult as objects move faster. In the present study, we show that this trade-off may not be due to increased speed itself but may, instead, be due to the increased crowding that usually accompanies increases in speed. Here, we isolate changes in speed from variations in crowding, by projecting a tracking display either onto a small area at the center of a hemispheric projection dome or onto the entire dome. Use of the larger display increased retinal image size and object speed by a factor of 4 but did not increase interobject crowding. Results showed that tracking accuracy was equally good in the large-display condition, even when the objects traveled far into the visual periphery. Accuracy was also not reduced when we tested object speeds that limited performance in the small-display condition. These results, along with a reinterpretation of past studies, suggest that we might be able to track multiple moving objects as fast as we can a single moving object, once the effect of object crowding is eliminated. The visual system takes a 2-D array of light and creates representations that support useful action in a 3-D world. One of these representations focuses on selecting and monitoring objects over time (Pylyshyn & Storm,

thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. There has been a growth in demand for advancing algorithms in surveillance applications concerning moving vehicles where analysis of traffic has a potential application to security, traffic management (congestion and accident detection), speed measurement, car counting and statistics, as well as turning movement at intersections. This research focuses on multiple-vehicle detection, recognition, and tracking in urban environments based on video sequences obtained from a single CCD camera mounted on a pole at urban highways and crossroads. The proposed system integrates several modules including segmentation, object detection, object recognition and classification, and tracking. Background segmentation, based on Gaussian Mixture models, is used to extract moving objects from images using the respective foreground object information such as location, size, and color distribution. To recognize vehicles, a 3D polyhedral car model described by a set

Attention mechanisms for counting in

Abstract not found