On detection of multiple object instances using hough transforms

Barinova, Olga; Lempitsky, Victor; Kohli, Pushmeet

doi:10.1109/cvpr.2010.5539905

Cited by 112 publications

(93 citation statements)

References 18 publications

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“…Gall and Lempitsky 2 proposed the Hough forest to build decision trees in a supervised manner, where a set of leaves can be regarded as a discriminative codebook that produces probabilistic votes with better voting performance. Barinova et al 4 proposed an MAP inference method rather than nonmaximum suppression (NMS) to seek the maxima in the Hough image. Wang et al 5 proposed a structured Hough transform method that incorporates depth-dependent contexts into a codebook-based pedestrian detection model.…”

Section: Hough Transform Methodsmentioning

confidence: 99%

“…The advantage of the Hough transform methods is that they can detect pedestrians with low computational cost due to the simple structure 9 and can also locate a partially occluded pedestrian in an image using a small set of local patches. 1,[3][4][5] The implicit shaped model (ISM) 1 has been widely derived by other Hough transformbased methods, which constructs a visual codebook by clustering local features in an unsupervised manner. Gall and Lempitsky 2 proposed the Hough forest to build decision trees in a supervised manner, where a set of leaves can be regarded as a discriminative codebook that produces probabilistic votes with better voting performance.…”

Section: Hough Transform Methodsmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9][10] The applicability of the Hough transform framework can be attributed to its robustness against partial occlusions, as indicated in Refs. 1 and 3-5.…”

Section: Introductionmentioning

confidence: 99%

“…The Hough transform framework for pedestrian detection includes three primary steps: (i) construct visual codebook, (ii) cast probabilistic votes for object center into a Hough image according to the codebook using voting elements of the test image, and (iii) search maxima in the Hough image as object hypotheses. Although some Hough transform methods demonstrate the significance of the visual codebook and voting weights 1,2,4 for detection performance, none use contextual information. Voting elements, which denote the image patches classified into object categories, cast probabilistic votes into a Hough image.…”

Section: Introductionmentioning

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%

Section: Hough Transform Methodsmentioning

confidence: 99%

“…[1][2][3][4][5][6][7][8][9][10] The applicability of the Hough transform framework can be attributed to its robustness against partial occlusions, as indicated in Refs. 1 and 3-5.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…paper V H algorithms applications Zhu and Yuille (1996) semi-metric per-label region merging unsupervised segmentation Torr (1998) × per-label expectation maximization + pruning model selection, motion estimation metric, semi-metric × α-expansion, αβ-swap stereo, denoising Kolmogorov (2006) arbitrary × tree-reweighted message passing stereo Li (2007) × per-label LP relaxation + rounding motion estimation Lazic et al (2009) × per-label belief propagation motion estimation Kumar and Koller (2009) r-HST metric × hierarchical graph cuts denoising, scene registration Delong et al (2010) metric, semi-metric any subsets α-expansion, αβ-swap, greedy FL homography detection, motion estimation, unsupervised segmentation Barinova et al (2010) × per-label greedy facility location (FL) object detection Ladický et al (2010a) metric, semi-metric parsimonious * α-expansion, αβ-swap object recognition this work h-metric h-subsets h-fusion w/ α-expansion unsupervised segmentation better approximation bound. The improved theoretical guarantees are important because, in practice, α-expansion can easily get stuck in poor local minima for this useful class of energies; to the best of our knowledge, our h-fusion algorithm is state of the art.…”

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confidence: 99%

Minimizing Energies with Hierarchical Costs

et al. 2012

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Computer vision is full of problems elegantly expressed in terms of energy minimization. We characterize a class of energies with hierarchical costs and propose a novel hierarchical fusion algorithm. Hierarchical costs are natural for modeling an array of difficult problems. For example, in semantic segmentation one could rule out unlikely object combinations via hierarchical context. In geometric model estimation, one could penalize the number of unique model families in a solution, not just the number of models-a kind of hierarchical MDL criterion. Hierarchical fusion uses the well-known α-expansion algorithm as a subroutine, and offers a much better approximation bound in important cases.

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Class-Agnostic Counting

Xie

Zisserman

2019

Lecture Notes in Computer Science

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Nearly all existing counting methods are designed for a specific object class. Our work, however, aims to create a counting model able to count any class of object. To achieve this goal, we formulate counting as a matching problem, enabling us to exploit the image selfsimilarity property that naturally exists in object counting problems. We make the following three contributions: first, a Generic Matching Network (GMN) architecture that can potentially count any object in a class-agnostic manner; second, by reformulating the counting problem as one of matching objects, we can take advantage of the abundance of video data labeled for tracking, which contains natural repetitions suitable for training a counting model. Such data enables us to train the GMN. Third, to customize the GMN to different user requirements, an adapter module is used to specialize the model with minimal effort, i.e. using a few labeled examples, and adapting only a small fraction of the trained parameters. This is a form of few-shot learning, which is practical for domains where labels are limited due to requiring expert knowledge (e.g. microbiology). We demonstrate the flexibility of our method on a diverse set of existing counting benchmarks: specifically cells, cars, and human crowds. The model achieves competitive performance on cell and crowd counting datasets, and surpasses the state-of-the-art on the car dataset using only three training images. When training on the entire dataset, the proposed method outperforms all previous methods by a large margin.

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On detection of multiple object instances using hough transforms

Cited by 112 publications

References 18 publications

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

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

Minimizing Energies with Hierarchical Costs

Class-Agnostic Counting

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