Near-Infrared-Based Nighttime Pedestrian Detection Using Grouped Part Models

Lee, Yi-Shu; Chan, Yi-Ming; Fu, Li‐Chen; Hsiao, Pei‐Yung

doi:10.1109/tits.2014.2385707

Cited by 32 publications

(8 citation statements)

References 23 publications

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“…For instance, in [2], a histogram of oriented gradient (HOG) was used to dig out the shape information. In [3], a gradient location orientation histogram (GLOH) was presented to extract the gradient features. In [4], a modified local binary pattern (LBP) was proposed to extract the texture feature for infrared targets.…”

Section: Introductionmentioning

confidence: 99%

Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning

2020

KSII TIIS

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Infrared pedestrian target recognition is a vital problem of significant interest in computer vision. In this work, a novel infrared pedestrian target recognition method that uses heterogeneous features with multi-kernel transfer learning is proposed. Firstly, to exploit the characteristics of infrared pedestrian targets fully, a novel multi-scale monogenic filtering-based completed local binary pattern descriptor, referred to as MSMF-CLBP, is designed to extract the texture information, and then an improved histogram of oriented gradient-fisher vector descriptor, referred to as HOG-FV, is proposed to extract the shape information. Second, to enrich the semantic content of feature expression, these two heterogeneous features are integrated to get more complete representation for infrared pedestrian targets. Third, to overcome the defects, such as poor generalization, scarcity of tagged infrared samples, distributional and semantic deviations between the training and testing samples, of the state-of-the-art classifiers, an effective multi-kernel transfer learning classifier called MK-TrAdaBoost is designed. Experimental results show that the proposed method outperforms many state-of-the-art recognition approaches for infrared pedestrian targets.

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Section: Introductionmentioning

confidence: 99%

Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning

2020

KSII TIIS

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“…In the faint light, the visible light sensor cannot obtain a clear image of the scene. The infrared sensor [8,9] can capture infrared radiation of the target, and any substance can produce infrared radiation when temperature is above absolute zero. Therefore, the infrared sensor can be well utilized in the low-light level (even in the dark) and has ability to preserve the contour feature of the object scene.…”

Section: Introductionmentioning

confidence: 99%

Integrative Multi-Spectral Sensor Device for Far-Infrared and Visible Light Fusion

et al. 2018

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Infrared and visible light image fusion technology is a hot spot in the research of multi-sensor fusion technology in recent years. Existing infrared and visible light fusion technologies need to register before fusion because of using two cameras. However, the application effect of the registration technology has yet to be improved. Hence, a novel integrative multi-spectral sensor device is proposed for infrared and visible light fusion, and by using the beam splitter prism, the coaxial light incident from the same lens is projected to the infrared charge coupled device (CCD) and visible light CCD, respectively. In this paper, the imaging mechanism of the proposed sensor device is studied with the process of the signals acquisition and fusion. The simulation experiment, which involves the entire process of the optic system, signal acquisition, and signal fusion, is constructed based on imaging effect model. Additionally, the quality evaluation index is adopted to analyze the simulation result. The experimental results demonstrate that the proposed sensor device is effective and feasible.

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“…Another kind of system is active, i.e., Near-Infrared (NIR): NIR pedestrian detection systems are able to detect a human because human skin reflects strong NIR light [ 12 ]. There are some effective pedestrian detection algorithms using NIR cameras, such as Lee [ 13 ], who proposes an NIR-based nighttime pedestrian detection system using the grouped part models method. This method aims to deal with the problem that the pedestrian is partially invisible because clothes on the pedestrian might absorb most of the infrared.…”

Section: Introductionmentioning

confidence: 99%

“…From these images, it can be seen that there are plenty of challenges to achieving an effective nighttime pedestrian detection system. Firstly, the color discrimination of the image is decreased because the illumination at night is weak [ 13 ]. Furthermore, owing to the gray value of the obtained image being dim, the image has less texture information and blurred contours.…”

Section: Introductionmentioning

confidence: 99%

Nighttime Foreground Pedestrian Detection Based on Three-Dimensional Voxel Surface Model

Zhang

Wei

et al. 2017

Sensors

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Pedestrian detection is among the most frequently-used preprocessing tasks in many surveillance application fields, from low-level people counting to high-level scene understanding. Even though many approaches perform well in the daytime with sufficient illumination, pedestrian detection at night is still a critical and challenging problem for video surveillance systems. To respond to this need, in this paper, we provide an affordable solution with a near-infrared stereo network camera, as well as a novel three-dimensional foreground pedestrian detection model. Specifically, instead of using an expensive thermal camera, we build a near-infrared stereo vision system with two calibrated network cameras and near-infrared lamps. The core of the system is a novel voxel surface model, which is able to estimate the dynamic changes of three-dimensional geometric information of the surveillance scene and to segment and locate foreground pedestrians in real time. A free update policy for unknown points is designed for model updating, and the extracted shadow of the pedestrian is adopted to remove foreground false alarms. To evaluate the performance of the proposed model, the system is deployed in several nighttime surveillance scenes. Experimental results demonstrate that our method is capable of nighttime pedestrian segmentation and detection in real time under heavy occlusion. In addition, the qualitative and quantitative comparison results show that our work outperforms classical background subtraction approaches and a recent RGB-D method, as well as achieving comparable performance with the state-of-the-art deep learning pedestrian detection method even with a much lower hardware cost.

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Near-Infrared-Based Nighttime Pedestrian Detection Using Grouped Part Models

Cited by 32 publications

References 23 publications

Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning

Infrared Target Recognition using Heterogeneous Features with Multi-kernel Transfer Learning

Integrative Multi-Spectral Sensor Device for Far-Infrared and Visible Light Fusion

Nighttime Foreground Pedestrian Detection Based on Three-Dimensional Voxel Surface Model

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