Seeking the Strongest Rigid Detector

Benenson, Rodrigo; Mathias, Markus; Tuytelaars, Tinne; Gool, Luc Van

doi:10.1109/cvpr.2013.470

Cited by 229 publications

(181 citation statements)

References 15 publications

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“…[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39] Here, we review the methods based on the Hough transform framework 1,2,4-68-10 that are most relevant to our work.…”

Section: Hough Transform Methodsmentioning

confidence: 99%

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Improved Hough transform by modeling context with conditional random fields for partially occluded pedestrian detection

Jiang

Xiong

2018

Opt. Eng.

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Abstract. Traditional Hough transform-based methods detect objects by casting votes to object centroids from object patches. It is difficult to disambiguate object patches from the background by a classifier without contextual information, as an image patch only carries partial information about the object. To leverage the contextual information among image patches, we capture the contextual relationships on image patches through a conditional random field (CRF) with latent variables denoted by locality-constrained linear coding (LLC). The strength of the pairwise energy in the CRF is measured using a Gaussian kernel. In the training stage, we modulate the visual codebook by learning the CRF model iteratively. In the test stage, the binary labels of image patches are jointly estimated by the CRF model. Image patches labeled as the object category cast weighted votes for object centroids in an image according to the LLC coefficients. Experimental results on the INRIA pedestrian, TUD Brussels, and Caltech pedestrian datasets demonstrate the effectiveness of the proposed method compared with other Hough transform-based methods. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Hough Transform Methodsmentioning

confidence: 99%

“…We use precision recall (PR) curve to evaluate pedestrian datasets. 4,26,28 Following, 9,28 we use average precision (AP) to measure detection performance on these datasets, which denotes the area under the PR curve. The AP was calculated in accordance with the criteria of PASCAL VOC.…”

Section: Experiments Proceduresmentioning

confidence: 99%

Improved Hough transform by modeling context with conditional random fields for partially occluded pedestrian detection

Jiang

Xiong

2018

Opt. Eng.

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“…Recent works show that most top performance detectors use AdaBoost based algorithm as classifier to achieve accurate pedestrian detection [15,16]. Comparing to support vector machine, AdaBoost is more efficient especially in the case of a large feature pool with multiple features, such as 10 channel features in [16].…”

Section: Cascade Classifier Trainingmentioning

confidence: 99%

“…Comparing to support vector machine, AdaBoost is more efficient especially in the case of a large feature pool with multiple features, such as 10 channel features in [16]. Therefore, the cascade AdaBoost algorithm is adopted to train pedestrian classifier in this work.…”

Section: Cascade Classifier Trainingmentioning

confidence: 99%

Pedestrian Detection for Android Mobile Devices

Li¹,

Qu²,

Ma³

2018

dtcse

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With the popularity of Android mobile devices and rapid development of hardware performance, more computer vision algorithms can be implemented on Android mobile devices, such as object detection, face recognition, etc. This paper focuses on the problem of implementing pedestrian detection on Android mobile devices. Recently, the commonly used pedestrian detection algorithm is based on sliding window strategy which uses single or multi-features, such as HOG and Channel features. Due to the resource limitation of mobile devices, in this paper, we use HOG and LBP joint feature to achieve pedestrian detection. In order to improve the efficiency of the algorithm, we use the spatial pooling algorithm to process the HOG-LBP joint feature. Experiments on the Android mobile phone shows that this method not only has competitive accuracy but also improves the pedestrian detection efficiency on both INRIA dataset and mobile phone images.

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“…This pivotal paper started an era where robust descriptors such as HoGs, SIFT and their fast counterparts such as SURF features, densely or sparsely measured all over the image have been concatenated and fed to a classifier. These very simple schemes achieve competitive pedestrian and face detection performance [100,26,101]. The application of these robust features with cascades of weak classifiers and boosting methodologies has recently started to receive attention.…”

Section: Robust Descriptors Meet Boostingmentioning

confidence: 99%