Pedestrian Detection Algorithm based on Improved Faster RCNN

The emergence and development of deep learning theory in machine learning field provide new method for visual-based pedestrian recognition technology. To achieve better performance in this application, an improved weakly supervised hierarchical deep learning pedestrian recognition algorithm with two-dimensional deep belief networks is proposed. The improvements are made by taking into consideration the weaknesses of structure and training methods of existing classifiers. First, traditional one-dimensional deep belief network is expanded to two-dimensional that allows image matrix to be loaded directly to preserve more information of a sample space. Then, a determination regularization term with small weight is added to the traditional unsupervised training objective function. By this modification, original unsupervised training is transformed to weakly supervised training. Subsequently, that gives the extracted features discrimination ability. Multiple sets of comparative experiments show that the performance of the proposed algorithm is better than other deep learning algorithms in recognition rate and outperforms most of the existing state-of-the-art methods in non-occlusion pedestrian data set while performs fair in weakly and heavily occlusion data set.

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Pedestrian detection algorithm based on improved Yolo v3

10.1109/icpics52425.2021.9524267 ◽

2021 ◽

Author(s):

Jiaqi Zhang ◽

Xunlei Chen ◽

Yingling Li ◽

Tianxiang Chen ◽

Liqiang Mou

Keyword(s):

Pedestrian Detection ◽

Detection Algorithm

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Combined attention mechanism and CenterNet pedestrian detection algorithm

2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) ◽

10.1109/iaeac50856.2021.9391037 ◽

2021 ◽

Author(s):

Shuangcai Liu ◽

Hui Ma

Keyword(s):

Pedestrian Detection ◽

Detection Algorithm ◽

Attention Mechanism

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Pedestrian detection algorithm based on video sequences and laser point cloud

Frontiers of Computer Science ◽

10.1007/s11704-014-3413-2 ◽

2014 ◽

Vol 9 (3) ◽

pp. 402-414 ◽

Cited By ~ 2

Author(s):

Hui Li ◽

Yun Liu ◽

Shengwu Xiong ◽

Lin Wang

Keyword(s):

Point Cloud ◽

Pedestrian Detection ◽

Detection Algorithm ◽

Video Sequences

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Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

Security and Communication Networks ◽

10.1155/2021/1549772 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Zhaoli Wu ◽

Xin Wang ◽

Chao Chen

Keyword(s):

Real Time ◽

Target Detection ◽

Pedestrian Detection ◽

Infrared Image ◽

Far Infrared ◽

Detection Algorithm ◽

Model Parameters ◽

Detection Accuracy ◽

Detection Model ◽

Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

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Pedestrian detection algorithm based on improved muti-scale feature fusion

Journal of Physics Conference Series ◽

10.1088/1742-6596/2078/1/012008 ◽

2021 ◽

Vol 2078 (1) ◽

pp. 012008

Author(s):

Hui Liu ◽

Keyang Cheng

Keyword(s):

Clustering Algorithm ◽

Feature Fusion ◽

Pedestrian Detection ◽

Detection Algorithm ◽

Data Sets ◽

False Detection ◽

Scale Feature ◽

Multi Scale ◽

Dilated Convolution ◽

Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

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