Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection

Watanabe, Takahiro; Ito, Satoshi; Yokoi, Kazuhito

doi:10.1007/978-3-540-92957-4_4

Cited by 190 publications

(95 citation statements)

References 20 publications

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“…To start off, traditional approach is described based on the method developed by Dalal [8] and extended by several other researchers [9], [10]. It is based on a fixedsize sliding window detection algorithm.…”

Section: Review Of Existing Workmentioning

confidence: 99%

Static and Dynamic Pedestrian Detection Algorithm for Visual Based Driver Assistive System

Bush

Dimililer

2017

ITM Web Conf.

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Abstract. This paper presents a new pedestrian detection algorithm used in Advanced Driver-Assistance System with only one camera aiming to improving traffic safety. The new pedestrian detection algorithm differs from traditional pedestrian detection algorithm, which only focuses on pedestrian detection rate or pedestrian detection accuracy. Conversely, the proposed algorithm focuses on both the accuracy and the rate. Some new features are proposed to improve pedestrian detection rate of the system. Also color difference was used to decrease the false detecting rate. The experimental results show that the pedestrian detection rate can be around 90% and the false detecting rate is 3%.

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Section: Review Of Existing Workmentioning

confidence: 99%

Static and Dynamic Pedestrian Detection Algorithm for Visual Based Driver Assistive System

Bush

Dimililer

2017

ITM Web Conf.

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“…S-CoHOG.The spatial CoHOG is initially introduced in the context of pedestrian detection [27]. Specially, it uses pairs of gradient orientations as units and employs the cooccurrence matrix for image representation.…”

Section: Spatial Context Descriptorsmentioning

confidence: 99%

Exploring Motion Boundary based Sampling and Spatial-Temporal Context Descriptors for Action Recognition

Peng¹,

Qiao²,

Peng³

et al. 2013

Procedings of the British Machine Vision Conference 2013

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Feature representation is important for human action recognition. Recently, Wang et al. [25] proposed dense trajectory (DT) based features for action video representation and achieved state-of-the-art performance on several action datasets. In this paper, we improve the DT method in two folds. Firstly, we introduce a motion boundary based dense sampling strategy, which greatly reduces the number of valid trajectories while preserves the discriminative power. Secondly, we develop a set of new descriptors which describe the spatial-temporal context of motion trajectories. To evaluate the performance of the proposed methods, we conduct extensive experiments on three benchmarks including K-TH, YouTube and HMDB51. The results show that our sampling strategy significantly reduces the computational cost of point tracking without degrading performance. Meanwhile, we achieve superior performance than the state-of-the-art methods by utilizing our spatial-temporal context descriptors.

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“…The first source is the idea of using appearance information, whose importance in object detection has been widely discussed such as in [1,7,8], and specially the idea of combining cues with the HOG descriptor, e.g., co-occurrence HOG [9], color HOG [10], etc. The second source is the popularity of using GLCM on texture image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Semantic Image Labeling with Histograms of Oriented Gradient and Gray Level Co-occurrence Matrix

Ma¹,

Liu

Huang³

et al. 2017

Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

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Abstract-In this paper, we propose a new approach for semantic image labeling by incorporating texture, gradient and color information. In our paper, the texture information is extracted by Gray Level Co-occurrence Matrix (GLCM). The gradient information is obtained by Histograms of Oriented Gradients (HOG). We apply the HOG, GLCM descriptors with color information simultaneously to enrich the image features of different information. To utilizing these features more effectively, we use the Approximate Nearest Neighbors (ANN) algorithm for clustering. After obtaining these information, the Joint Boost algorithm is applied to give an effective classifier by training many weak learner classifiers. At the end, a set of experiments with one descriptor or several descriptors combined are made to evaluating the performance of our method.

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Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection

Cited by 190 publications

References 20 publications

Static and Dynamic Pedestrian Detection Algorithm for Visual Based Driver Assistive System

Static and Dynamic Pedestrian Detection Algorithm for Visual Based Driver Assistive System

Exploring Motion Boundary based Sampling and Spatial-Temporal Context Descriptors for Action Recognition

Semantic Image Labeling with Histograms of Oriented Gradient and Gray Level Co-occurrence Matrix

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