Unconstrained Multiple Face Tracking Using Convolutional Neural Networks

2021 ◽  
pp. 429-437
Author(s):  
B. B. Ramakrishna ◽  
Sharmila Kumari
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Face Tracking

A Face Tracking Method in Videos Based on Convolutional Neural Networks

2018 ◽  
Vol 32 (12) ◽  
pp. 1856017 ◽  
Author(s):  
Zihan Ren ◽  
Jianwei Li ◽  
Xiaoying Zhang ◽  
Shuangyuan Yang ◽  
Fuhao Zou
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Convolutional Neural Networks ◽  
Processing Speed ◽  
Face Tracking ◽  
Light Change ◽  
Complex Scenes ◽  
Fast Processing ◽  
Kalman Filter Algorithm ◽  
Realistic Significance

Face tracking in surveillance videos is one of the important issues in the field of computer vision and has realistic significance. In this paper, a new face tracking framework in videos based on convolutional neural networks (CNNs) and Kalman filter algorithm is proposed. The framework uses a rough-to-fine CNN to detect faces in each frame of the video. The rough-to-fine CNN method has a higher accuracy in complex scenes such as face rotation, light change and occlusion. When face tracking fails due to severe occlusion or significant rotation, the framework uses Kalman filter to predict face position. The experimental results show that the proposed method has high precision and fast processing speed.

Mouse face tracking using convolutional neural networks

2017 ◽  
Vol 12 (2) ◽  
pp. 153-161 ◽  
Author(s):  
İbrahim Batuhan Akkaya ◽  
Ugur Halici
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Face Tracking

Traffic Lights Detection in adverse conditions using Convolutional Neural Networks

2018 ◽  
Author(s):  
George Symeonidis ◽  
Peter P. Groumpos ◽  
Evangelos Dermatas
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Traffic Lights ◽  
Adverse Conditions
Sign in / Sign up

Export Citation Format

Share Document