Human Detection Using Wavelet-Based CS-LBP and a Cascade of Random Forests

Kim, Deok-Yeon; Kwak, Joon-Young; Ko, Byoung Chul; Nam, Jae-Yeal

doi:10.1109/icme.2012.124

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…27) are popular features to discriminate humans from backgrounds. Recently, the LBP texture operator has been successfully used in various computer vision applications, such as face recognition, 28 human detection, 29 and human tracking, 30 because it is robust against illumination changes, very fast to compute, and does not require many parameters. 31 LBP describes the gray-scale local texture of the image with low computational complexity by using a simple method.…”

Section: Oriented Center-symmetric Lbpmentioning

confidence: 99%

Human tracking in thermal images using adaptive particle filters with online random forest learning

Kwak

Nam

2013

Opt. Eng.

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This paper presents a fast and robust human tracking method to use in a moving long-wave infrared thermal camera under poor illumination with the existence of shadows and cluttered backgrounds. To improve the human tracking performance while minimizing the computation time, this study proposes an online learning of classifiers based on particle filters and combination of a local intensity distribution (LID) with oriented centersymmetric local binary patterns (OCS-LBP). Specifically, we design a realtime random forest (RF), which is the ensemble of decision trees for confidence estimation, and confidences of the RF are converted into a likelihood function of the target state. First, the target model is selected by the user and particles are sampled. Then, RFs are generated using the positive and negative examples with LID and OCS-LBP features by online learning. The learned RF classifiers are used to detect the most likely target position in the subsequent frame in the next stage. Then, the RFs are learned again by means of fast retraining with the tracked object and background appearance in the new frame. The proposed algorithm is successfully applied to various thermal videos as tests and its tracking performance is better than those of other 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: Oriented Center-symmetric Lbpmentioning

confidence: 99%

Human tracking in thermal images using adaptive particle filters with online random forest learning

Kwak

Nam

2013

Opt. Eng.

Self Cite

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“…To overcome this problem, Zheng et al 7 used centersymmetric LBPs (CS-LBPs) for pedestrian detection. Kim et al 8 proposed an approach based on the combination of a wavelet-based CS-LBP (WCS-LBP) with a cascade of random forests. Three types of WCS-LBP features are extracted from the scanning window of wavelet-transformed subimages to reduce the feature dimension.…”

Section: Related Workmentioning

confidence: 99%

Human object annotation for surveillance video forensics

Fraz

Zafar

Tzanidou

et al. 2013

J. Electron. Imaging

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A system that can automatically annotate surveillance video in a manner useful for locating a person with a given description of clothing is presented. Each human is annotated based on two appearance features: primary colors of clothes and the presence of text/logos on clothes. The annotation occurs after a robust foreground extraction stage employing a modified Gaussian mixture model-based approach. The proposed pipeline consists of a preprocessing stage where color appearance of an image is improved using a color constancy algorithm. In order to annotate color information for human clothes, we use the color histogram feature in HSV space and find local maxima to extract dominant colors for different parts of a segmented human object. To detect text/logos on clothes, we begin with the extraction of connected components of enhanced horizontal, vertical, and diagonal edges in the frames. These candidate regions are classified as text or nontext on the basis of their local energy-based shape histogram features. Further, to detect humans, a novel technique has been proposed that uses contourlet transform-based local binary pattern (CLBP) features. In the proposed method, we extract the uniform direction invariant LBP feature descriptor for contourlet transformed high-pass subimages from vertical and diagonal directional bands. In the final stage, extracted CLBP descriptors are classified by a trained support vector machine. Experimental results illustrate the superiority of our method on large-scale surveillance video data.

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“…In this study, we extracted more effective and compact features by extending our initial method 15 in several ways to speed up the computation and improve the detection performance as following ways:…”

mentioning

confidence: 99%

Three-level cascade of random forests for rapid human detection

Kim

Jung

et al. 2013

Opt. Eng

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Abstract. We propose a novel human detection approach that combines three types of center symmetric local binary patterns (CS-LBP) descriptors with a cascade of random forests (RFs). To detect human regions in a lowdimensional feature space, we first extract three types of CS-LBP features from the scanning window of a downsampled saliency texture map and two wavelet-transformed subimages. The extracted CS-LBP descriptors are applied to a three-level cascade of RFs, which combines a series of RF classifiers as a filter chain. The three-level cascade of RFs with CS-LBPs delivers rapid human detection with higher detection accuracy, as compared with combinations of other features and classifiers. The proposed algorithm is successfully applied to various human and nonhuman images from the INRIA dataset, and it performs better than other related algorithms.

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Human Detection Using Wavelet-Based CS-LBP and a Cascade of Random Forests

Cited by 8 publications

References 15 publications

Human tracking in thermal images using adaptive particle filters with online random forest learning

Human tracking in thermal images using adaptive particle filters with online random forest learning

Human object annotation for surveillance video forensics

Three-level cascade of random forests for rapid human detection

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