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Computer Vision Systems: 8th International Conference, ICVS by Birgit Möller, Oliver Greß, Stefan Posch (auth.), James L.

By Birgit Möller, Oliver Greß, Stefan Posch (auth.), James L. Crowley, Bruce A. Draper, Monique Thonnat (eds.)

This booklet constitutes the refereed court cases of the eighth overseas convention on computing device imaginative and prescient platforms, ICVS 2011, held in Sophia Antipolis, France, in September 2009. The 22 revised papers awarded have been conscientiously reviewed and chosen from fifty eight submissions. The papers are geared up in topical sections on imaginative and prescient structures, keep an eye on of notion, functionality assessment, task reputation, and data directed vision.

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Example text

It is based on an original object oriented architecture, that allows the combination of numerous sensors and landmark types, and the integration of various approaches proposed in the literature. The system capacities are illustrated by the presentation of an inertial/vision SLAM approach, for which several improvements over existing methods have been introduced, and that copes with very high dynamic motions. Results with a hand-held camera are presented. 1 Motivation Progresses in image processing, the growth of available computing power and the proposition of approaches to deal with bearings-only observations have made visual SLAM very popular, particularly since the demonstration of a real-time implementation by Davison in 2003 [4].

For the experiments in this section, we use the Bahnhof sequence from the Zurich Mobile Pedestrian corpus [4], and employ ground planes estimated by SfM. The sequence consists of 999 frames of size 640×480, containing 5,193 annotated pedestrians with a height > 60 pixels. We start by evaluating computational effort on the first 100 frames. We vary the start scale and ground plane corridor size and report the number of blocks and SVM windows evaluated, as well as the average run-time per frame (Tab.

0). The results shown in Fig. 2 fppi). Usually, the upscaling step comes at considerable additional cost. groundHOG can achieve significant computational savings here, since it can limit the upscaling operation to a (relatively small) band around the horizon line. As Tab. 1 shows, groundHOG can still process the upscaled images at 20 fps (23 fps if the width of the detection corridor is also bounded to 600 pixels), effectively the same run-time as the unconstrained detector on the original images.

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