Efficient Combination of Histograms for Real-Time Tracking Using Mean-Shift and Trust-Region Optimization

2005 ◽  
pp. 254-261 ◽  
Author(s):  
F. Bajramovic ◽  
Ch. Gräßl ◽  
J. Denzler
Keyword(s):  
Real Time ◽  
Mean Shift ◽  
Trust Region ◽  
Real Time Tracking

Research on Target Tracking Algorithm Based on Particle Filter and Mean-Shift

2013 ◽  
Vol 457-458 ◽  
pp. 1050-1053
Author(s):  
Yan Hai Wu ◽  
Xia Min Xie ◽  
Zi Shuo Han
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Real Time ◽  
Mean Shift ◽  
Extreme Value ◽  
Tracking Algorithm ◽  
Real Time Tracking ◽  
The Mean ◽  
Particle Filter Algorithm ◽  
Local Extreme

Since Mean-Shift tracking algorithm always falls into local extreme value when the target was sheltered and the particle filter tracking algorithm has huge calculation and degeneracy phenomenon, a new target tracking algorithm based on Mean-Shift and Particle Filter combination is proposed in this paper. First, this paper introduces the basic theory of Mean-Shift and Particle Filter tracking algorithm, and then presents the new target tracking which the Mean-Shift iteration embeds Particle Filter algorithm. Experiment results show that the algorithm needs less computation, while the real-time tracking has been guaranteed, robustness has been improved and the tracking results has been greatly increased.

Research of CamShift Algorithm Used in Object Tracking of Selfvision Underwater Robot

2013 ◽  
Vol 662 ◽  
pp. 971-974
Author(s):  
Wei Xiang
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Mean Shift ◽  
Underwater Robot ◽  
Background Color ◽  
Mean Shift Algorithm ◽  
Experimental Platform ◽  
Camshift Algorithm ◽  
Tracking Process ◽  
Real Time Tracking

It is difficult for self-vision underwater robot to track object, and the tracking process is frequently inaccurate, unstable or even loss goals. To solve the above questions, Continuously Adaptive Mean Shift Algorithm (CamShift) is used in object tracking of self-vision underwater robot in this paper. We build a software experimental platform by VC++6.0 and Opencv1.0, with the external camera to capture video, and then apply Camshift algorithm in the environment, in which background color is not similar to the object to realize the real time tracking. The experimental results show the effectiveness of the algorithm for self-vision underwater robot.

An Overview of Data Aggregation Architecture for Real-Time Tracking with Sensor Networks

2006 ◽  
Author(s):  
Tian He ◽  
Lin Gu ◽  
Liqian Luo ◽  
Ting Yan ◽  
John A. Stankovic ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Real Time ◽  
Data Aggregation ◽  
Real Time Tracking

scholarly journals Enhancing spatial perception through sound: mapping human movements into MIDI

2021 ◽  
Author(s):  
Bernardo Breve ◽  
Stefano Cirillo ◽  
Mariano Cuofano ◽  
Domenico Desiato
Keyword(s):  
Real Time ◽  
User Study ◽  
Spatial Perception ◽  
Human Movements ◽  
Different Types ◽  
Real Time Tracking ◽  
People’S Perceptions ◽  
Novel Algorithm

AbstractGestural expressiveness plays a fundamental role in the interaction with people, environments, animals, things, and so on. Thus, several emerging application domains would exploit the interpretation of movements to support their critical designing processes. To this end, new forms to express the people’s perceptions could help their interpretation, like in the case of music. In this paper, we investigate the user’s perception associated with the interpretation of sounds by highlighting how sounds can be exploited for helping users in adapting to a specific environment. We present a novel algorithm for mapping human movements into MIDI music. The algorithm has been implemented in a system that integrates a module for real-time tracking of movements through a sample based synthesizer using different types of filters to modulate frequencies. The system has been evaluated through a user study, in which several users have participated in a room experience, yielding significant results about their perceptions with respect to the environment they were immersed.

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