Multi-pedestrian tracking based on hierarchical Siamese Neural Network

2021 ◽  
Author(s):  
Miao Ma ◽  
Yuntao Wang ◽  
Haipeng Pan ◽  
RUSTAM STOLKIN
Keyword(s):  
Neural Network ◽  
Pedestrian Tracking

An Improved Multi-Pedestrian Tracking System Based on Deep Neural Network

2019 ◽  
Author(s):  
Yuan Gong ◽  
Jianning Chi ◽  
Xiaosheng Yu ◽  
Chengdong Wu ◽  
Yifei Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Tracking System ◽  
Pedestrian Tracking

scholarly journals Scale-Adaptive KCF Mixed with Deep Feature for Pedestrian Tracking

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 536
Author(s):  
Yang Zhou ◽  
Wenzhu Yang ◽  
Yuan Shen
Keyword(s):  
Neural Network ◽  
Template Matching ◽  
Detection Algorithm ◽  
Important Research ◽  
Pedestrian Tracking ◽  
Histogram Of Oriented Gradient ◽  
Deep Feature ◽  
Kernel Correlation ◽  
Gradient Feature ◽  
Small Improvement

Pedestrian tracking is an important research content in the field of computer vision. Tracking is achieved by predicting the position of a specific pedestrian in each frame of a video. Pedestrian tracking methods include neural network-based methods and traditional template matching-based methods, such as the SiamRPN (Siamese region proposal network), the DASiamRPN (distractor-aware SiamRPN), and the KCF (kernel correlation filter). The KCF algorithm has no scale-adaptive capability and cannot effectively solve the occlusion problem, and because of many defects of the HOG (histogram of oriented gradient) feature that the KCF uses, the tracking target is easy to lose. For those defects of the KCF algorithm, an improved KCF model, the SKCFMDF (scale-adaptive KCF mixed with deep feature) algorithm was designed. By introducing deep features extracted by a newly designed neural network and by introducing the YOLOv3 (you only look once version 3) object detection algorithm, which was also improved for more accurate detection, the model was able to achieve scale adaptation and to effectively solve the problem of occlusion and defects of the HOG feature. Compared with the original KCF, the success rate of pedestrian tracking under complex conditions was increased by 36%. Compared with the mainstream SiamRPN and DASiamRPN models, it was still able to achieve a small improvement.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural
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