2008 Ninth International Workshop on Image Analysis for Multimedia Interactive Services 2008
DOI: 10.1109/wiamis.2008.8
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Robust People Detection by Fusion of Evidence from Multiple Methods

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Cited by 7 publications
(10 citation statements)
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“…In this section, we describe the experiments performed over the experimental dataset and including different approaches that cover all the people detection issues identified from the state of the art. We have selected eight diverse people detection approaches: Edge [24], Fusion [18], HOG [43], ISM [57], TUD [51], DTDP [52], ACF [49] and IMM [40]. According to the chosen object detection approach, Edge combines segmentation and exhaustive search, Fusion is based only on segmentation and the rest of them are based on exhaustive search.…”
Section: Resultsmentioning
confidence: 99%
“…In this section, we describe the experiments performed over the experimental dataset and including different approaches that cover all the people detection issues identified from the state of the art. We have selected eight diverse people detection approaches: Edge [24], Fusion [18], HOG [43], ISM [57], TUD [51], DTDP [52], ACF [49] and IMM [40]. According to the chosen object detection approach, Edge combines segmentation and exhaustive search, Fusion is based only on segmentation and the rest of them are based on exhaustive search.…”
Section: Resultsmentioning
confidence: 99%
“…In this section, we describe the experiments carried out for testing the proposed people system over our video dataset and we compare the results of our approach Edge, with three other people detectors approaches from the state of the art: two non-real time approaches, HOG and TUD detectors [3,7], and one real time approach, Fusion [9]. Our approach Edge, is based on [28], the authors themselves show in [29] similar results than HOG in terms of classification accuracy.…”
Section: Resultsmentioning
confidence: 99%
“…Most of the existing approaches are only based on appearance information [3,7,9,13,16,17,28,30,32] although some of them add robustness to the detection incorporating motion information through tracking algorithms. There are few approaches based only on motion information [6,22] which main advantages are that they are independent of appearance variability and usually have low complexity.…”
Section: State Of the Artmentioning
confidence: 99%
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“…This module calculates the confidence of being people for each blob. This module is structured as described in [18]. It is based on a fusion approach that combines the data provided by simple people detectors.…”
Section: System Overviewmentioning
confidence: 99%