A concurrent neural network approach to pedestrian detection in thermal imagery

Neagoe, Victor-Emil; Ciotec, Adrian-Dumitru; Barar, Andrei-Petru

doi:10.1109/iccomm.2012.6262539

Cited by 18 publications

(17 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%

“…The last stage that finally validates the object is a classifier. The most common classifiers are: support vector machine (SVM) as an example of the supervised learning method, artificial neural networks, self-organizing maps (SOMs), and matrices of neurons [12]. A very helpful algorithm during classification is the boosting algorithm.…”

Section: Video Processing Algorithmsmentioning

confidence: 99%

“…For the RFB kernel the two parameters: γ (see (15)) and C (see (12)) should be tuned in order to find the optimal solution. The authors used a procedure described in [15], known as cross-validation.…”

Section: Video Processing Algorithmsmentioning

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%

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Video Processing Algorithms for Detection of Pedestrians

Piniarski

Pawlowski

Dąbrowski

2015

CMST

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“…Several researches using single laser scanner sensors [9], [10] and some others using multiple laser sensors [11], [12]. Meanwhile, the research of [8] used far infrared sensors that can detect objects in low resolution (long distance) as well as color and texture are less clear. For an environment with poor lighting, such as at night, often used stereo cameras that have night vision features [13].…”

Section: A Input Devicesmentioning

confidence: 99%

“…Some object classification algorithms currently used algorithms, from the simple to the complex. Examples of object classification algorithm that is widely used is the Support Vector Machine (SVM) [5]- [7] and neural networks [2], [8].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Pedestrian Detection in Video

Solichin¹,

Harjoko²,

Putra³

2014

ijacsa

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Abstract-Pedestrian detection is one of the important topics in computer vision with key applications in various fields of human life such as intelligent vehicles, surveillance and advanced robotics. In recent years, research related to pedestrian detection commonplace. This paper aims to review the papers related to pedestrian detection in order to provide an overview of the recent research. Main contribution of this paper is to provide a general overview of pedestrian detection process that is viewed from different sides of the discussion. We divide the discussion into three stages: input, process and output. This paper does not make a selection or technique best method and optimal because the best technique depends on the needs, concerns and existing environment. However, this paper is useful for future researchers who want to know the current researches related to pedestrian detection.

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Visual and Thermal Data for Pedestrian and Cyclist Detection

Ahmed

Huda

Rajbhandari

et al. 2019

Lecture Notes in Computer Science

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With the continued advancement of autonomous vehicles and their implementation in public roads, accurate detection of vulnerable road users (VRUs) is vital for ensuring safety. To provide higher levels of safety for these VRUs, an effective detection system should be employed that can correctly identify VRUs in all types of environments (e.g. VRU appearance, crowded scenes) and conditions (e.g. fog, rain, night-time). This paper presents optimal methods of sensor fusion for pedestrian and cyclist detection using Deep Neural Networks (DNNs) for higher levels of feature abstraction. Typically, visible sensors have been utilised for this purpose. Recently, thermal sensors system or combination of visual and thermal sensors have been employed for pedestrian detection with advanced detection algorithm. DNNs have provided promising results for improving the accuracy of pedestrian and cyclist detection. This is because they are able to extract features at higher levels than typical hand-crafted detectors. Previous studies have shown that amongst the several sensor fusion techniques that exist, Halfway Fusion has provided the best results in terms of accuracy and robustness. Although sensor fusion and DNN implementation have been used for pedestrian detection, there is considerably less research undertaken for cyclist detection.

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A concurrent neural network approach to pedestrian detection in thermal imagery

Cited by 18 publications

References 9 publications

Video Processing Algorithms for Detection of Pedestrians

Video Processing Algorithms for Detection of Pedestrians

A Survey of Pedestrian Detection in Video

Visual and Thermal Data for Pedestrian and Cyclist Detection

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