Detection of pedestrians at night time using learning-based method and head validation

Liu, Qiong; Zhuang, Jiajun; Kong, Shufeng

doi:10.1109/ist.2012.6295596

Cited by 12 publications

(12 citation statements)

References 9 publications

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“…Automatic pedestrian detection is a relatively new area of digital video processing but, as it is very important, it grows rapidly. Both passive [9] and active [10][11][12][13] systems are used for night vision solutions. Most of them use trainable algorithms, like artificial neural networks (ANNs) [13], support vector machines (SVMs) [9,11], etc.…”

Section: Video Processing Algorithmsmentioning

confidence: 99%

“…In order to produce uniform areas with clear edges, the actual thresholding algorithm must smoothly pass through neighboring pixels with values close to the threshold. For this purpose the hysteresis threshold technique with T L (i, j)lower threshold and T H (i, j) -upper threshold, is used [9].…”

Section: Video Processing Algorithmsmentioning

confidence: 99%

“…In another paper [13] ever worse DR = 82.9%, FAR = 6.5% are reported but they were obtained for a smaller resolution of 24×64 pixels. The algorithm presented in [9] is quite fast and works with 35 fps (frames per second). This confirms high efficiency of the linear solution for that kernel but the detection quality is worse.…”

Section: A Detector Training With the Ntpdmentioning

confidence: 99%

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Video Processing Algorithms for Detection of Pedestrians

Piniarski

Pawlowski

Dąbrowski

2015

CMST

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In this paper a video processing procedure for automatic detection of pedestrians is presented. It is planned to use it as a part of the automotive night vision system. Generally, such systems are either passive (i.e. those based on thermal vision) or active (i.e. equipped with illuminators and near infrared cameras). Passive systems provide a large range of detection, while their active counterparts, operating in a somehow smaller range, offer more readable images for car drivers. However, all images produced with both kinds of these systems are quite specific and special image processing procedures are needed for them. For this purpose the authors used modified and adapted algorithms, such as dual-threshold locally adaptive classification, connected component labeling, histogram of oriented gradients, and the support vector machine with a radial basic function kernel or with a linear kernel. Tests performed on the real night vision recordings show very high efficiency of the proposed solution with accuracy equal to 99.2% for the linear kernel and even to 99.36% for the radial basic function kernel.

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Section: Video Processing Algorithmsmentioning

confidence: 99%

Section: Video Processing Algorithmsmentioning

confidence: 99%

Section: A Detector Training With the Ntpdmentioning

confidence: 99%

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Video Processing Algorithms for Detection of Pedestrians

Piniarski

Pawlowski

Dąbrowski

2015

CMST

View full text Add to dashboard Cite

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“…There are also many researchers that focus on the similar problem of detecting pedestrians in an FIR image. Most of the proposed methods use a two-class classification framework and use different features such as HOG and different classifiers such as SVM and Adaboost [13,14].…”

Section: Introductionmentioning

confidence: 99%

Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning

et al. 2016

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The use of night vision systems in vehicles is becoming increasingly common. Several approaches using infrared sensors have been proposed in the literature to detect vehicles in far infrared (FIR) images. However, these systems still have low vehicle detection rates and performance could be improved. This paper presents a novel method to detect vehicles using a far infrared automotive sensor. Firstly, vehicle candidates are generated using a constant threshold from the infrared frame. Contours are then generated by using a local adaptive threshold based on maximum distance, which decreases the number of processing regions for classification and reduces the false positive rate. Finally, vehicle candidates are verified using a deep belief network (DBN) based classifier. The detection rate is 93.9% which is achieved on a database of 5000 images and video streams. This result is approximately a 2.5% improvement on previously reported methods and the false detection rate is also the lowest among them.

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“…In 2010, Besbes et al introduce it to vehicle detections in which a SURF feature and SVM classifier based method are proposed [9]. Similarly, this kind of machine learning based framework is also used in pedestrian detection in FIR image [10,11]. Feature selection and classifier design are two critical components for object detection.…”

Section: Introductionmentioning

confidence: 99%

Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning

et al. 2016

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Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.

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Detection of pedestrians at night time using learning-based method and head validation

Cited by 12 publications

References 9 publications

Video Processing Algorithms for Detection of Pedestrians

Video Processing Algorithms for Detection of Pedestrians

Night-Time Vehicle Sensing in Far Infrared Image with Deep Learning

Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning

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