A Novel Vehicle Tracking and Recognition System Based on Edge Line Segment Set Matching

2008 ◽  
Author(s):  
Tan Liu ◽  
Mei Xie
Keyword(s):  
Line Segment ◽  
Recognition System ◽  
Vehicle Tracking ◽  
Tracking And Recognition

scholarly journals A Real-time Face Tracking and Recognition System Based on SeetaFace

2020 ◽  
Vol 1673 ◽  
pp. 012043
Author(s):  
Dilizhati Yilihamu ◽  
Palidan Tuerxun ◽  
Abdusalam Dawut ◽  
Askar Hamdulla
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Face Tracking ◽  
Tracking And Recognition

scholarly journals Analysis and innovation prospect of intelligent ship visual tracking and recognition system

2021 ◽  
Vol 1982 (1) ◽  
pp. 012055
Author(s):  
Shiguan Yu ◽  
Hongshuo Zhang ◽  
Shilong Mu ◽  
Shizhong Liu ◽  
Haojie Ding
Keyword(s):  
Visual Tracking ◽  
Recognition System ◽  
Tracking And Recognition

scholarly journals Transfer Learning Based Traffic Sign Recognition System for Surveillance Application

2019 ◽  
Vol 8 (3) ◽  
pp. 573-576
Keyword(s):  
Transfer Learning ◽  
Road Traffic ◽  
Recognition System ◽  
Vehicle Tracking ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Wide Range ◽  
Traffic Recognition ◽  
Surveillance Application

Road Traffic Recognition is very important in many applications, such as automated deployment, traffic mapping, and vehicle tracking. Proposed traffic sign recognition system tails the transfer learning method that is frequently used in neural network uses. The benefit of expending this technique is that the initially network has been trained with a rich set of features appropriate to a wide range of images. Once the network is trained , learning can be transferred to the new activity adjustment to the network. Firsthand Indian traffic sign dataset is used.New results exhibit that the suggested method can accomplish modest outcomes when matched with other related techniques.

Research on the Real-Time Multiple Face Detection, Tracking and Recognition Based on Video

2013 ◽  
Vol 373-375 ◽  
pp. 442-446
Author(s):  
Hai Feng Sang ◽  
Chao Xu ◽  
Dan Yang Wu ◽  
Jing Huang
Keyword(s):  
Real Time ◽  
Face Detection ◽  
Recognition System ◽  
Detection Algorithm ◽  
Research Field ◽  
Face Tracking ◽  
Fusion Algorithm ◽  
The Real ◽  
Video Images ◽  
Tracking And Recognition

The video images of human face tracking and recognition is a hot research field of biometric recognition and artificial intelligence in recent years. This paper presents an automatic face tracking and recognition system, which can track multiple faces real-timely and recognize the identity. Aiming at Adaboost face detection algorithm is easy to false detection, presents a fusion algorithm based on Adaboost face detection algorithm and Active Shape Model. The algorithm is not only detect face real-timely but also remove the non-face areas; A multi thread CamShift tracking algorithm is proposed for many faces interlaced and face number of changes in the scene . Meanwhile, the algorithm also can identify the faces which have been tracked in the video. The experiment results show that the system is capable of improving the accurate rate of faces detection and recognition in complex backgrounds, and furthermore it also can track the real-time faces effectively.

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