Efficient and robust Pedestrian Detection using Deep Learning for Human-Aware Navigation

Mateus, André; Ribeiro, David Augusto; Miraldo, Pedro; Nascimento, Jacinto C.

doi:10.1016/j.robot.2018.12.007

Cited by 57 publications

(21 citation statements)

References 33 publications

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“…The evaluation indicators for performance comparison of the detection algorithm mainly include mAP value and the average processing time per frame. In reference [46], a pedestrian detection algorithm combining aggregate channel features and CNN was adopted. When only the ACF detector was used in comparison with other algorithms, although the average processing time was relatively short, the mAP value was the lowest.…”

Section: Performance Comparison Of Detection Algorithmsmentioning

confidence: 99%

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

Cao

Song

Peng

et al. 2020

Sensors

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Pedestrian detection is an important aspect of the development of intelligent vehicles. To address problems in which traditional pedestrian detection is susceptible to environmental factors and are unable to meet the requirements of accuracy in real time, this study proposes a pedestrian detection algorithm for intelligent vehicles in complex scenarios. YOLOv3 is one of the deep learning-based object detection algorithms with good performance at present. In this article, the basic principle of YOLOv3 is elaborated and analyzed firstly to determine its limitations in pedestrian detection. Then, on the basis of the original YOLOv3 network model, many improvements are made, including modifying grid cell size, adopting improved k-means clustering algorithm, improving multi-scale bounding box prediction based on receptive field, and using Soft-NMS algorithm. Finally, based on INRIA person and PASCAL VOC 2012 datasets, pedestrian detection experiments are conducted to test the performance of the algorithm in various complex scenarios. The experimental results show that the mean Average Precision (mAP) value reaches 90.42%, and the average processing time of each frame is 9.6 ms. Compared with other detection algorithms, the proposed algorithm exhibits accuracy and real-time performance together, good robustness and anti-interference ability in complex scenarios, strong generalization ability, high network stability, and detection accuracy and detection speed have been markedly improved. Such improvements are significant in protecting the road safety of pedestrians and reducing traffic accidents, and are conducive to ensuring the steady development of the technological level of intelligent vehicle driving assistance.

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Section: Performance Comparison Of Detection Algorithmsmentioning

confidence: 99%

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

Cao

Song

Peng

et al. 2020

Sensors

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“…Image segmentation is a computationally expensive process because it is done on the pixel level of the image. It has many practical applications in different fields, such as object detection [17], face and pedestrian detection and localization [50]. In the medical field, image segmentation is used to locate tumors [51], measure tissue volumes [52], and retinal vessels segmentation [53].…”

Section: Cnn-based Images Segmentationmentioning

confidence: 99%

Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

Soomro¹,

et al. 2019

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This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis. Many eye diseases often lead to blindness in the absence of proper clinical diagnosis and medical treatment. For example, diabetic retinopathy (DR) is one such disease in which the retinal blood vessels of human eyes are damaged. The ophthalmologists diagnose DR based on their professional knowledge, that is labor intensive. With the advances in image processing and artificial intelligence, computer vision-based techniques have been applied rapidly and widely in the field of medical images analysis and are becoming a better way to advance ophthalmology in practice. Such approaches utilize accurate visual analysis to identify the abnormality of blood vessels with improved performance over manual procedures. More recently, machine learning, in particular, deep learning, has been successfully implemented in this area. In this paper, we focus on recent advances in deep learning methods for retinal image analysis. We review the related publications since 1982, which include more than 80 papers for retinal vessels detections in the research scope spanning from segmentation to classification. Although deep learning has been successfully implemented in other areas, we found only 17 papers so far focus on retinal blood vessel segmentation. This paper characterizes each deep learning based segmentation method as described in the literature. Analyzing along with the limitations and advantages of each method. In the end, we offer some recommendations for future improvement for retinal image analysis. INDEX TERMS Retinal colour fundus images, convolutional neural networks, retinal vessels segmentation.

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“…This allows us to know the location of the pedestrian in the 3D environment, i.e., the pedestrian's position relative to the camera system can be obtained. This is a useful feature in human-awareness navigation [8], [44].…”

Section: B Contributionsmentioning

confidence: 99%

OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras

Pais

Dias

Nascimento

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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Pedestrian detection is one of the most explored topics in computer vision and robotics. The use of deep learning methods allowed the development of new and highly competitive algorithms. Deep Reinforcement Learning has proved to be within the state-of-the-art in terms of both detection in perspective cameras and robotics applications. However, for detection in omnidirectional cameras, the literature is still scarce, mostly because of their high levels of distortion. This paper presents a novel and efficient technique for robust pedestrian detection in omnidirectional images. The proposed method uses deep Reinforcement Learning that takes advantage of the distortion in the image. By considering the 3D bounding boxes and their distorted projections into the image, our method is able to provide the pedestrian's position in the world, in contrast to the image positions provided by most state-of-the-art methods for perspective cameras. Our method avoids the need of preprocessing steps to remove the distortion, which is computationally expensive. Beyond the novel solution, our method compares favorably with the state-of-the-art methodologies that do not consider the underlying distortion for the detection task.1 Although undistorted images keep the perspective projection constraints (i.e., straight lines in the world will be projected into straight lines in the image), the objects will be stretched. This means that the objects should not be approximated by regular bounding boxes, which are used in most of the DL techniques for object detection (we note that there are alternatives that do not consider regular bounding boxes in [19]).

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Efficient and robust Pedestrian Detection using Deep Learning for Human-Aware Navigation

Cited by 57 publications

References 33 publications

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

Pedestrian Detection Algorithm for Intelligent Vehicles in Complex Scenarios

Deep Learning Models for Retinal Blood Vessels Segmentation: A Review

OmniDRL: Robust Pedestrian Detection using Deep Reinforcement Learning on Omnidirectional Cameras

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