scholarly journals People Counting System Combined with Multi-Feature Fusion Algorithm

2016 ◽  
Vol 05 (03) ◽  
pp. 95-104
Author(s):  
宏广 李
Keyword(s):  
Feature Fusion ◽  
Fusion Algorithm ◽  
Counting System ◽  
People Counting

RSNet: Rail semantic segmentation network for extracting aerial railroad images

2021 ◽  
pp. 1-18
Author(s):  
R.S. Rampriya ◽  
Sabarinathan ◽  
R. Suganya
Keyword(s):  
Real Time ◽  
Visual Processing ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Vital Role ◽  
Obstacle Detection ◽  
Aerial Images ◽  
Computationally Efficient ◽  
Fusion Algorithm ◽  
Uav Images

In the near future, combo of UAV (Unmanned Aerial Vehicle) and computer vision will play a vital role in monitoring the condition of the railroad periodically to ensure passenger safety. The most significant module involved in railroad visual processing is obstacle detection, in which caution is obstacle fallen near track gage inside or outside. This leads to the importance of detecting and segment the railroad as three key regions, such as gage inside, rails, and background. Traditional railroad segmentation methods depend on either manual feature selection or expensive dedicated devices such as Lidar, which is typically less reliable in railroad semantic segmentation. Also, cameras mounted on moving vehicles like a drone can produce high-resolution images, so segmenting precise pixel information from those aerial images has been challenging due to the railroad surroundings chaos. RSNet is a multi-level feature fusion algorithm for segmenting railroad aerial images captured by UAV and proposes an attention-based efficient convolutional encoder for feature extraction, which is robust and computationally efficient and modified residual decoder for segmentation which considers only essential features and produces less overhead with higher performance even in real-time railroad drone imagery. The network is trained and tested on a railroad scenic view segmentation dataset (RSSD), which we have built from real-time UAV images and achieves 0.973 dice coefficient and 0.94 jaccard on test data that exhibits better results compared to the existing approaches like a residual unit and residual squeeze net.

Development of people-counting system with human-information sensor using multi-element pyroelectric infrared array detector

1997 ◽  
Vol 58 (2) ◽  
pp. 165-171 ◽  
Author(s):  
Kazuhiko Hashimoto ◽  
Makoto Yoshinomoto ◽  
Satoshi Matsueda ◽  
Katsuya Morinaka ◽  
Nobuyuki Yoshiike
Keyword(s):  
Array Detector ◽  
Counting System ◽  
People Counting ◽  
Infrared Array

scholarly journals An Improved TextRank Multi-feature Fusion Algorithm For Keyword Extraction of Educational Resources

2021 ◽  
Vol 2078 (1) ◽  
pp. 012021
Author(s):  
Hongyang Zhao ◽  
Qiang Xie
Keyword(s):  
Chinese Text ◽  
Feature Fusion ◽  
Graph Model ◽  
Recall Rate ◽  
Educational Resources ◽  
Keyword Extraction ◽  
Semantic Features ◽  
Fusion Algorithm ◽  
General Semantic

Abstract In view of the fact that the traditional graph model method which only considers statistical features or general semantic features when extracting keywords from existing massive educational resources, lacks the function of mining and utilizing multi-factor semantic features, this paper proposes an improved TextRank-based algorithm for keyword extraction of educational resources. According to the characteristics of Chinese text and the shortcomings of traditional TextRank algorithm, the improved algorithm featuring multi-feature fusion is developed using the importance of words in the corpus, the location information in the text and the attributes of words. Experimental results show that this method has higher accuracy, recall rate, and F-measure value than traditional algorithms in the process of keyword extraction of educational resources, which improves the quality of keyword extraction and is beneficial to better utilization and management of educational resources.

Anti-Occlusion Algorithm for Object Tracking Based on Multi-Feature Fusion

2014 ◽  
Vol 610 ◽  
pp. 393-400
Author(s):  
Jie Cao ◽  
Xuan Liang
Keyword(s):  
Object Tracking ◽  
Feature Fusion ◽  
Experimental Results ◽  
Occlusion Detection ◽  
Fusion Algorithm ◽  
Fusion Strategy ◽  
Complex Background ◽  
Complex Scenes

Complex background, especially when the object is similar to the background in color or the target gets blocked, can easily lead to tracking failure. Therefore, a fusion algorithm based on features confidence and similarity was proposed, it can adaptively adjust fusion strategy when occlusion occurs. And this confidence is used among occlusion detection, to overcome the problem of inaccurate occlusion determination when blocked by analogue. The experimental results show that the proposed algorithm is more robust in the case of the cover, but also has good performance under other complex scenes.

Epileptic Classification with Deep Transfer Learning based Feature Fusion Algorithm

2021 ◽  
pp. 1-1
Author(s):  
Jiuwen Cao ◽  
Dinghan Hu ◽  
Yaomin Wang ◽  
Jianzhong Wang ◽  
Baiying Lei
Keyword(s):  
Transfer Learning ◽  
Feature Fusion ◽  
Fusion Algorithm
Sign in / Sign up

Export Citation Format

Share Document