Fast Human Detection Using a Cascade of Histograms of Oriented Gradients

Zhu, Qiaoming; Yeh, Mei-Chen; Cheng, Kwang-Ting; Avidan, Shai

doi:10.1109/cvpr.2006.119

Cited by 656 publications

(100 citation statements)

References 12 publications

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“…In the HV step, the proposed system use two kinds of feature vectors: HOG [30,31] and HOG symmetry [29]. The HOG is one of the most popular features in vision-based target object detection fields, especially human detection systems.…”

Section: Featurementioning

confidence: 99%

“…The system applies the knowledge-based HG method which extracts hypotheses using shadow regions [9,29]. In the HV step, the system applies an appearance-based method which uses the histogram of oriented gradients (HOG) [30,31] and HOG symmetry vectors [29] as feature vectors and applies total error rate minimization using reduced model (TER-RM) [29,32] as a classifier.…”

Section: Introductionmentioning

confidence: 99%

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Vision-Based Vehicle Detection System Applying Hypothesis Fitting

Cheon¹,

Lee²

2017

IJFIS

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In this paper, we propose the improved vision-based vehicle detection system which is added a hypothesis fitting (HF) step to the typical vehicle detection system consisting of the hypothesis generation (HG) and hypothesis verification (HV) step. In the HG step, the system generates hypotheses using shadow regions appearing under vehicles. In the HV step, the system verifies whether a hypothesis is a vehicle or not by applying a classifier and feature vectors extracted from the hypothesis. The proposed HF step is conducted between HG and HV steps and help to improve performance of the vehicle verification by adapting region of hypotheses. We verify the performance of our proposed HF method through a total of 4,797 hypotheses, 1,606 positive hypotheses, and 3,191 negative hypotheses data set.

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Section: Featurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vision-Based Vehicle Detection System Applying Hypothesis Fitting

Cheon¹,

Lee²

2017

IJFIS

View full text Add to dashboard Cite

show abstract

“…To illustrate the speed of the detector, we list the number of NOGCF features and time cost units in the first 5 stages compared to the fast HOG [11] in Table 1, since both methods use cascaded classifiers structure whose speed is determined by several frontal stages. Each fast HOG feature is 36D and costs 36 time units per feature, while NOGCF costs only 1 time unit.…”

Section: Evaluation Of the Detectors On Image Datasetsmentioning

confidence: 99%

“…HOG [1] or fast HOG [11] is a multi-dimensional histogram (e.g. 36 bins) and then inner product should be performed with SVM or LDA classifier, so it's computational intensive.…”

Section: Scalar Feature Constructionmentioning

confidence: 99%

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A Real-Time Human Detection System for Video

Zeng

Wang

Lin

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYThis work presents a real-time human detection system for VGA (Video Graphics Array, 640 × 480) video, which well suits visual surveillance applications. To achieve high running speed and accuracy, firstly we design multiple fast scalar feature types on the gradient channels, and experimentally identify that NOGCF (Normalized Oriented Gradient Channel Feature) has better performance with Gentle AdaBoost in cascaded classifiers. A confidence measure for cascaded classifiers is developed and utilized in the subsequent tracking stage. Secondly, we propose to use speedup techniques including a detector pyramid for multi-scale detection and channel compression for integral channel calculation respectively. Thirdly, by integrating the detector's discrete detected humans and continuous detection confidence map, we employ a two-layer tracking by detection algorithm for further speedup and accuracy improvement. Compared with other methods, experiments show the system is significantly faster with 20 fps running speed in VGA video and has better accuracy as well.

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Multi-label classification for colon cancer using histopathological images

Jiao

Wang

et al. 2013

Microsc. Res. Tech.

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Colon cancer classification has a significant guidance value in clinical diagnoses and medical prognoses. The classification of colon cancers with high accuracy is the premise of efficient treatment. Our task is to build a system for colon cancer detection and classification based on slide histopathological images. Some former researches focus on single label classification. Through analyzing large amount of colon cancer images, we found that one image may contain cancer regions of multiple types. Therefore, we reformulated the task as multi-label problem. Four kinds of features (Color Histogram, Gray-Level Co-occurrence Matrix, Histogram of Oriented Gradients and Euler number) were introduced to compose our discriminative feature set, extracted from our dataset that includes six single categories and four multi-label categories. In order to evaluate the performance and make comparison with our multi-label model, three commonly used multi-classification methods were designed in our experiment including one-against-all SVM (OAA), one-against-one SVM (OAO) and multi-structure SVM. Four indicators (Precision, Recall, F-measure, and Accuracy) under 3-fold cross-validation were used to validate the performance of our approach. Experiment results show that the precision, recall and F-measure of multi-label method as 73.7%, 68.2%, and 70.8% with all features, which are higher than the other three classifiers. These results demonstrate the effectiveness and efficiency of our method on colon histopathological images analysis.

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Fast Human Detection Using a Cascade of Histograms of Oriented Gradients

Cited by 656 publications

References 12 publications

Vision-Based Vehicle Detection System Applying Hypothesis Fitting

Vision-Based Vehicle Detection System Applying Hypothesis Fitting

A Real-Time Human Detection System for Video

Multi-label classification for colon cancer using histopathological images

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