Improving pedestrian safety using combined HOG and Haar partial detection in mobile systems

Mihçioğlu, Muhammed Enis; Alkar, Ali Ziya

doi:10.1080/15389588.2019.1624731

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Machine learning algorithms focus on feature extraction and classifiers [92]. For feature extraction, techniques such as Histogram of Oriented Gradients [93][94][95][96][97][98][99][100], Local Binary Pattern [101][102][103][104][105][106][107], Deformable Part Model [108][109][110][111][112][113], and Aggregate Channel Feature (ACF) [114][115][116][117][118] are included. On the other hand, methods such as Support Vector Machine (SVM) [94,105,[119][120][121][122], Decision Tree [123][124][125][126], Random Forest (RF) [127][128][129][130][131][132] and Ada-Boost [81,119,133,134] are used for ...…”

Section: Object Detection and Classificationmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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There is a group of users within the vehicular traffic ecosystem known as Vulnerable Road Users (VRUs). VRUs include pedestrians, cyclists, motorcyclists, among others. On the other hand, connected autonomous vehicles (CAVs) are a set of technologies that combines, on the one hand, communication technologies to stay always ubiquitous connected, and on the other hand, automated technologies to assist or replace the human driver during the driving process. Autonomous vehicles are being visualized as a viable alternative to solve road accidents providing a general safe environment for all the users on the road specifically to the most vulnerable. One of the problems facing autonomous vehicles is to generate mechanisms that facilitate their integration not only within the mobility environment, but also into the road society in a safe and efficient way. In this paper, we analyze and discuss how this integration can take place, reviewing the work that has been developed in recent years in each of the stages of the vehicle-human interaction, analyzing the challenges of vulnerable users and proposing solutions that contribute to solving these challenges.

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Section: Object Detection and Classificationmentioning

confidence: 99%

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Reyes-Muñoz

Ibáñez

2022

Sensors

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“…Among these problems, partial occlusion frequently occurs due to the diversity of the partially occluded patterns of the pedestrians, and it needs further investigation between the pedestrians and the crowded instances. Handcrafted feature representations were previously used by the following traditional pedestrian detectors: Haar [7], [8], scale invariant feature transform (SIFT) [9], [10], histogram of oriented gradient (HOG) [11]- [13], and local binary pattern (LBP) [7] [14]. To perform pedestrian classification, these feature representations are combined with classifiers such as support vector machine (SVM) [15], [16] boosted forests [10], and AdaBoost [17].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian classification on transfer learning based deep convolutional neural network for partial occlusion handling

Thu

Suvonvorn

Kittiphattanabawon

2023

IJECE

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The investigation of a deep neural network for pedestrian classification using transfer learning methods is proposed in this study. The development of deep convolutional neural networks has significantly improved the autonomous driver assistance system for pedestrian classification. However, the presence of partially occluded parts and the appearance variation under complex scenes are still robust to challenge in the pedestrian detection system. To address this problem, we proposed six transfer learning models: end-to-end convolutional neural network (CNN) model, scratch-trained residual network (ResNet50) model, and four transfer learning models: visual geometry group 16 (VGG16), GoogLeNet (InceptionV3), ResNet50, and MobileNet. The performance of the pedestrian classification was evaluated using four publicly datasets: Institut National de Recherche en Sciences et Technologies du Numérique (INRIA), Prince of Songkla University (PSU), CVC05, and Walailak University (WU) datasets. The experimental results show that six transfer learning models achieve classification accuracy of 65.2% (end-to-end CNN), 92.92% (scratch-trained ResNet50), 97.15% (pre-trained VGG16), 94.39% (pre-trained InceptionV3), 90.43% (pre-trained ResNet50), and 98.69% (pre-trained MobileNet) using data from Southern Thailand (PSU dataset). Further analysis reveals that the deeper the ConvNet architecture, the more specific information of features is provided. In addition, the deep ConvNet architecture can distinguish pedestrian occluded patterns while being trained with partially occluded parts of data samples.

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“…Moreover, monocular camera images have been leveraged by deep learning approaches to detect both 2D and 3D objects. For example, [3,4] proposed perception systems to extract the semantic information from a front-view RGB image in the agricultural field and to warn the driver before a possible pedestrian collision. However, normal cameras can be easily affected by illumination changes or occlusions, which make them less appropriate in dealing with different environmental surroundings through day and night.…”

Section: Introductionmentioning

confidence: 99%

3D Pedestrian Detection in Farmland by Monocular RGB Image and Far-Infrared Sensing

Tian¹,

Deng²,

Yin³

et al. 2021

Remote Sensing

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The automated driving of agricultural machinery is of great significance for the agricultural production efficiency, yet is still challenging due to the significantly varied environmental conditions through day and night. To address operation safety for pedestrians in farmland, this paper proposes a 3D person sensing approach based on monocular RGB and Far-Infrared (FIR) images. Since public available datasets for agricultural 3D pedestrian detection are scarce, a new dataset is proposed, named as “FieldSafePedestrian”, which includes field images in both day and night. The implemented data augmentations of night images and semi-automatic labeling approach are also elaborated to facilitate the 3D annotation of pedestrians. To fuse heterogeneous images of sensors with non-parallel optical axis, the Dual-Input Depth-Guided Dynamic-Depthwise-Dilated Fusion network (D5F) is proposed, which assists the pixel alignment between FIR and RGB images with estimated depth information and deploys a dynamic filtering to guide the heterogeneous information fusion. Experiments on field images in both daytime and nighttime demonstrate that compared with the state-of-the-arts, the dynamic aligned image fusion achieves an accuracy gain of 3.9% and 4.5% in terms of center distance and BEV-IOU, respectively, without affecting the run-time efficiency.

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Improving pedestrian safety using combined HOG and Haar partial detection in mobile systems

Cited by 8 publications

References 6 publications

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Vulnerable Road Users and Connected Autonomous Vehicles Interaction: A Survey

Pedestrian classification on transfer learning based deep convolutional neural network for partial occlusion handling

3D Pedestrian Detection in Farmland by Monocular RGB Image and Far-Infrared Sensing

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