PFF-FPN: A Parallel Feature Fusion Module Based on FPN in Pedestrian Detection

Feature-based pedestrian detection method is currently the mainstream direction to solve the problem of pedestrian detection. In this kind of method, whether the appropriate feature can be extracted is the key to the comprehensive performance of the whole pedestrian detection system. It is believed that the appearance of a pedestrian can be better captured by the combination of edge/local shape feature and texture feature. In this field, the current method is to simply concatenate HOG (histogram of oriented gradient) features and LBP (local binary pattern) features extracted from an image to produce a new feature with large dimension. This kind of method achieves better performance at the cost of increasing the number of features. In this paper, Choquet integral based on the signed fuzzy measure is introduced to fuse HOG and LBP descriptors in parallel that is expected to improve accuracy without increasing feature dimensions. The parameters needed in the whole fusion process are optimized by a training algorithm based on genetic algorithm. This architecture has three advantages. Firstly, because the fusion of HOG and LBP features is parallel, the dimensions of the new features are not increased. Secondly, the speed of feature fusion is fast, thus reducing the time of pedestrian detection. Thirdly, the new features after fusion have the advantages of HOG and LBP features, which is helpful to improve the detection accuracy. The series of experimentation with the architecture proposed in this paper reaches promising and satisfactory results.

Download Full-text

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.

Download Full-text

Pedestrian detection in underground mines via parallel feature transfer network

Pattern Recognition ◽

10.1016/j.patcog.2020.107195 ◽

2020 ◽

Vol 103 ◽

pp. 107195 ◽

Cited By ~ 1

Author(s):

Xing Wei ◽

Haitao Zhang ◽

Shaofan Liu ◽

Yang Lu

Keyword(s):

Pedestrian Detection ◽

Underground Mines ◽

Parallel Feature

Download Full-text

Soccer Video Structure Analysis by Parallel Feature Fusion Network and Hidden-to-Observable Transferring Markov Model

IEEE Access ◽

10.1109/access.2017.2769140 ◽

2017 ◽

Vol 5 ◽

pp. 27322-27336 ◽

Cited By ~ 6

Author(s):

Mehrnaz Fani ◽

Mehran Yazdi ◽

David A. Clausi ◽

Alexander Wong

Keyword(s):

Markov Model ◽

Structure Analysis ◽

Feature Fusion ◽

Soccer Video ◽

Parallel Feature ◽

Video Structure Analysis

Download Full-text

Automated Atrial Fibrillation Detection Based on Feature Fusion Using Discriminant Canonical Correlation Analysis

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6691177 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Jingjing Shi ◽

Chao Chen ◽

Hui Liu ◽

Yinglong Wang ◽

Minglei Shu ◽

...

Keyword(s):

Atrial Fibrillation ◽

Correlation Analysis ◽

Canonical Correlation Analysis ◽

Canonical Correlation ◽

Expert Knowledge ◽

Feature Fusion ◽

Intraclass Correlation ◽

Single Feature ◽

Disability Rate ◽

Parallel Feature

Atrial fibrillation (AF) is one of the most common cardiovascular diseases, with a high disability rate and mortality rate. The early detection and treatment of atrial fibrillation have great clinical significance. In this paper, a multiple feature fusion is proposed to screen out AF recordings from single lead short electrocardiogram (ECG) recordings. The proposed method uses discriminant canonical correlation analysis (DCCA) feature fusion. It fully takes intraclass correlation and interclass correlation into consideration and solves the problem of computation and information redundancy with simple series or parallel feature fusion. The DCCA integrates traditional features extracted by expert knowledge and deep learning features extracted by the residual network and gated recurrent unit network to improve the low accuracy of a single feature. Based on the Cardiology Challenge 2017 dataset, the experiments are designed to verify the effectiveness of the proposed algorithm. In the experiments, the F1 index can reach 88%. The accuracy, sensitivity, and specificity are 91.7%, 90.4%, and 93.2%, respectively.

Download Full-text