Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Lifespans

2007 ◽  
Author(s):  
Yuan Li ◽  
Haizhou Ai ◽  
Takayoshi Yamashita ◽  
Shihong Lao ◽  
Masato Kawade
Keyword(s):  
Particle Filter ◽  
Frame Rate ◽  
Low Frame Rate

scholarly journals Particle Filter Tracking without Dynamics

2007 ◽  
Vol 4 (4) ◽  
pp. 169-177 ◽  
Author(s):  
Jaime Ortegon-Aguilar ◽  
Eduardo Bayro-Corrochano
Keyword(s):  
Computer Vision ◽  
Particle Filter ◽  
Background Subtraction ◽  
Particle Filters ◽  
Frame Rate ◽  
People Tracking ◽  
Human Machine Interaction ◽  
Controlled Environments ◽  
Low Frame Rate ◽  
Machine Interaction

People tracking is an interesting topic in computer vision. It has applications in industrial areas such as surveillance or human-machine interaction. Particle Filters is a common algorithm for people tracking; challenging situations occur when the target's motion is poorly modelled or with unexpected motions. In this paper, an alternative to address people tracking is presented. The proposed algorithm is based in particle filters, but instead of using a dynamical model, it uses background subtraction to predict future locations of particles. The algorithm is able to track people in omnidirectional sequences with a low frame rate (one or two frames per second). Our approach can tackle unexpected discontinuities and changes in the direction of the motion. The main goal of the paper is to track people from laboratories, but it has applications in surveillance, mainly in controlled environments.

Tracking in Low Frame Rate Video: A Cascade Particle Filter with Discriminative Observers of Different Life Spans

2008 ◽  
Vol 30 (10) ◽  
pp. 1728-1740 ◽  
Author(s):  
Yuan Li ◽  
Haizhou Ai ◽  
T. Yamashita ◽  
Shihong Lao ◽  
M. Kawade
Keyword(s):  
Particle Filter ◽  
Frame Rate ◽  
Low Frame Rate

scholarly journals Estimating Body Related Soft Biometric Traits in Video Frames

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Olasimbo Ayodeji Arigbabu ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Salman Yussof ◽  
Vahab Iranmanesh ◽  
...  
Keyword(s):  
Human Subjects ◽  
Visual Surveillance ◽  
Frame Rate ◽  
Visual Appearance ◽  
Single View ◽  
Soft Biometrics ◽  
Related Information ◽  
Video Frames ◽  
Low Frame Rate ◽  
Biometric Information

Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames.

Feasibility of pulse wave velocity estimation from low frame rate US sequences in vivo

2017 ◽  
Author(s):  
Maria Zontak ◽  
Matthew Bruce ◽  
Michelle Hippke ◽  
Alan Schwartz ◽  
Matthew O'Donnell
Keyword(s):  
Pulse Wave Velocity ◽  
Wave Velocity ◽  
Pulse Wave ◽  
Velocity Estimation ◽  
Frame Rate ◽  
Low Frame Rate
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