Design and Implementation of Campus Pedestrian Detection based on Unmanned Robot “Sweeper”

Shen, Yunfu; Zhang, Lixiao; Ma, Huinan; Kuan, Ta Wen; Zou, Tianyi

doi:10.1109/icot56925.2022.10008125

Cited by 2 publications

(1 citation statement)

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“…To enhance the reliability of detectable content and achieve real-time detection rates, the authors utilized a combination of visual cues, edge-based features, and color information as the basis for training a cascaded random forest (RF) classifier for detecting local contour cues in pedestrian images. Shen et al [21] proposed a method for campus pedestrian image detection using HSV thresholding binarization, image morphology processing, and image contour detection fitting. The method involves the use of erosion and extension operations, along with the adjustment of different rectangular structure elements, to reduce noise in the surroundings and extract campus pedestrian contours.…”

Section: Deep Learning-based Pedestrian Detection Contour Extraction ...mentioning

confidence: 99%

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Zhan

Zhu

et al. 2023

Entropy

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Infrared pedestrian target detection is affected by factors such as the low resolution and contrast of infrared pedestrian images, as well as the complexity of the background and the presence of multiple targets occluding each other, resulting in indistinct target features. To address these issues, this paper proposes a method to enhance the accuracy of pedestrian target detection by employing contour information to guide multi-scale feature detection. This involves analyzing the shapes and edges of the targets in infrared images at different scales to more accurately identify and differentiate them from the background and other targets. First, we propose a preprocessing method to suppress background interference and extract color information from visible images. Second, we propose an information fusion residual block combining a U-shaped structure and residual connection to form a feature extraction network. Then, we propose an attention mechanism based on a contour information-guided approach to guide the network to extract the depth features of pedestrian targets. Finally, we use the clustering method of mIoU to generate anchor frame sizes applicable to the KAIST pedestrian dataset and propose a hybrid loss function to enhance the network’s adaptability to pedestrian targets. The extensive experimental results show that the method proposed in this paper outperforms other comparative algorithms in pedestrian detection, proving its superiority.

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Section: Deep Learning-based Pedestrian Detection Contour Extraction ...mentioning

confidence: 99%

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Zhan

Zhu

et al. 2023

Entropy

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Cande: A Model for Predicting the Risk of Campus Violence in an Edge Intelligent Computing Architecture

Zhou,

Lu,

Huang

et al. 2024

2024 IEEE 10th International Conference on Edge Computing and Scalable Cloud (EdgeCom)

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Design and Implementation of Campus Pedestrian Detection based on Unmanned Robot “Sweeper”

Cited by 2 publications

References 10 publications

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Contour Information-Guided Multi-Scale Feature Detection Method for Visible-Infrared Pedestrian Detection

Cande: A Model for Predicting the Risk of Campus Violence in an Edge Intelligent Computing Architecture

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