.png){kind=icon}
返回
Robust multi-modal pedestrian detection using deep convolutional neural network with ensemble learning model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2024.123527.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the realm of video surveillance systems, pedestrian detection (PD) is a crucial task that enables the identification and tracking of individuals, crowd monitoring, and people counting. However, accurate PD is a challenging task due to variations in the appearance and pose of pedestrians. With the advent of deep learning (DL) techniques, particularly convolutional neural networks (CNNs), computer vision (CV) methods for object classification and detection have been revolutionized. Effective PD depends on supervised learning, which requires large labeled datasets. This paper, we present pedestrian detection (PD) using a Robust Multi-modal Pedestrian Detection using a Deep Convolutional Neural Network with an Ensemble Learning (RMPD-DCNN-EL) model. This model effects computer vision and ensemble learning to accomplish precise pedestrian detection. The SimAM EfficientNet model with a stochastic gradient descent (SGD) optimizer is used for feature extraction, while ensemble classification is performed using three DL models are Nested Long Short-Term Memory (NLSTM), deep belief networks (DBNs), and extreme learning machine (ELM). To evaluate the effectiveness of tettapproach, we use the INRIA dataset, and the experimental results prove the greater performance of RMPD-DCNN-EL over other DL methods. The proposed method enables robust pedestrian detection despite modifications in arrival and pose, making it an operative solution for video surveillance systems in smart transport environments. Experimental results show that the detection accuracy is 99.30% of the proposed algorithm is considerably higher than that of the traditional convolutional neural network algorithm.
Keyword:
Pedestrian detection
Deep learning
Ensemble learning
Video surveillance
Computer vision
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network基于改进遗传算法和深度信念网络的物联网入侵检测.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review自动驾驶中图像和点云融合的深度学习: 综述OAAI
OAAI分享收藏A Part-Aware Multi-Scale Fully Convolutional Network for Pedestrian Detection用于行人检测的部分感知多尺度全卷积网络
PREAI
分享收藏分享收藏Design Guidelines on Deep Learning-based Pedestrian Detection Methods for Supporting Autonomous Vehicles支持自动驾驶汽车的基于深度学习的行人检测方法设计指南
PREAI
分享收藏FlexPDR: Fully Flexible Pedestrian Dead Reckoning Using Online Multimode Recognition and Time-Series DecompositionFlexPDR: 使用在线多模式识别和时间序列分解的完全灵活的行人航位推算
PREAI
分享收藏
查看更多内容