Pedestrian Detection Method Based on Faster R-CNN

Zhang, Hui; Du, Yu; Ning, Shurong; Zhang, Yonghua; Yang, Shuo; Du, Chao

doi:10.1109/cis.2017.00099

Cited by 29 publications

(12 citation statements)

References 4 publications

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“…This means landmarks could be detected but may not be precise or have high accuracy. Comparing to well-configured examples presented in [19][20][21], the accuracy of our Faster R-CNN was moderately lower. This reduction in detection accuracy is the cause of a lower localization accuracy, as landmark detection results are required to generate localization results in the Faster R-CNN localization method.…”

Section: Detection Experimentsmentioning

confidence: 77%

“…The increased speed of Faster R-CNN for object detection makes it suitable for real time applications [17,18]. Implementations of Faster R-CNN spread throughout various applications, such as the detection of cyclists in depth images [19], pedestrian detection from security cameras [20], and ship detection in remote sensing images that contain foggy scenes [21]. In [19][20][21] it is shown that Faster R-CNN has high accuracy (more than 80%), slightly higher than human volunteers that have approximately 75% accuracy [22].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization

Nilwong

Hossain²,

Kaneko

et al. 2019

Machines

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Outdoor mobile robot applications generally implement Global Positioning Systems (GPS) for localization tasks. However, GPS accuracy in outdoor localization has less accuracy in different environmental conditions. This paper presents two outdoor localization methods based on deep learning and landmark detection. The first localization method is based on the Faster Regional-Convolutional Neural Network (Faster R-CNN) landmark detection in the captured image. Then, a feedforward neural network (FFNN) is trained to determine robot location coordinates and compass orientation from detected landmarks. The second localization employs a single convolutional neural network (CNN) to determine location and compass orientation from the whole image. The dataset consists of images, geolocation data and labeled bounding boxes to train and test two proposed localization methods. Results are illustrated with absolute errors from the comparisons between localization results and reference geolocation data in the dataset. The experimental results pointed both presented localization methods to be promising alternatives to GPS for outdoor localization.

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Section: Detection Experimentsmentioning

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization

Nilwong

Hossain²,

Kaneko

et al. 2019

Machines

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“…The results demonstrated 23% miss rate of deep features as compared with 46% miss rate for hand-crafted HOG features. Recently, deep learning-based faster RCNN model has been employed by Zhang et al [2], and it was evaluated with a total of ten thousand training images and one thousand testing images. Experimental analysis indicated a 92.70% precision, and 87.60% recall were achieved.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

“…One promising solution to address such a challenge is by employing deep learning models. Recently, various deep learning-based approaches have been implemented such as Faster R-CNN [2], YOLO [3], and tiny-YOLO [4]. Motivated by the cheap cost of the drone and the success of these deep learning models in handling image-based object detection problems [2], this work is aiming to adopt Faster R-CNN to handle the problem of pedestrian detection from drone-based images.…”

Section: Introductionmentioning

confidence: 99%

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

et al. 2020

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Pedestrian detection from a drone-based images has many potential applications such as searching for missing persons, surveillance of illegal immigrants, and monitoring of critical infrastructure. However, it is considered as a very challenge computer vision problem due to the variations in camera point of view, distance from pedestrian, changes in illuminations and weather conditions, variation in the surrounding objects, as well as present of human-like objects. Recently, deep learningbased models are getting more attention, and they have proven a great success in many object detection problems such as the detection of faces, breast masses, and vehicles. As such, this work aims to develop a deep learning-based model that will be applied for the problem of pedestrian detection from a drone-based images. Particularly, faster region-based convolutional neural network (Faster R-CNN) will be used to search for the present of a pedestrian inside the captured drone-based images. To assess the performances, a total of 1500 images were collected by S30W drone and these images were captured at different places, with various views and weather conditions, and at daytime and night-time. Results show that Faster R-CNN was able to achieve a promising result with 98% precision, 99% recall, and 98% F1 measure. Further analysis has been conducted by comparing the outcomes of Faster R-CNN with YOLO deep model on UAV123 publicly available dataset. The reported results indicated that both detection models almost reported very similar results.

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“…The detection and tracking of pedestrians using these methods have been extensively studied (24). These research initiatives have used convolutional neural networks to track people for a variety of purposes which closely mirror the needs of trespassing, for example autonomous driving (25)(26)(27), traffic safety (28,29), and surveillance (30)(31)(32)(33)(34)(35). The variance in the literature consists in the adjustment of variables of a convolutional neural network (number of layers, orientation of layers, application of study, etc.)…”

Section: Ai For Trespass Detectionmentioning

confidence: 99%

Artificial Intelligence-Aided Automated Detection of Railroad Trespassing

Zaman

Ren

Liu

2019

Transportation Research Record: Journal of the Transportation R

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Trespassing is the leading cause of rail-related deaths and has been on the rise for the past 10 years. Detection of unsafe trespassing of railroad tracks is critical for understanding and preventing fatalities. Witnessing these events has become possible with the widespread deployment of large volumes of surveillance video data in the railroad industry. This potential source of information requires immense labor to monitor in real time. To address this challenge this paper describes an artificial intelligence (AI) framework for the automatic detection of trespassing events in real time. This framework was implemented on three railroad video live streams, a grade crossing and two right-of-ways, in the United States. The AI algorithm automatically detects trespassing events, differentiates between the type of violator (car, motorcycle, truck, pedestrian, etc.) and sends an alert text message to a designated destination with important information including a video clip of the trespassing event. In this study, the AI has analyzed hours of live footage with no false positives or missed detections yet. This paper and its subsequent studies aim to provide the railroad industry with state-of-the-art AI tools to harness the untapped potential of an existing closed-circuit television infrastructure through the real-time analysis of their data feeds. The data generated from these studies will potentially help researchers understand human factors in railroad safety research and give them a real-time edge on tackling the critical challenges of trespassing in the railroad industry.

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Pedestrian Detection Method Based on Faster R-CNN

Cited by 29 publications

References 4 publications

Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization

Deep Learning-Based Landmark Detection for Mobile Robot Outdoor Localization

Faster R-CNN Deep Learning Model for Pedestrian Detection from Drone Images

Artificial Intelligence-Aided Automated Detection of Railroad Trespassing

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