Kernel-Bandwidth Adaptation for Tracking Object Changing in Size

2004 ◽  
pp. 581-588 ◽  
Author(s):  
Ning-Song Peng ◽  
Jie Yang ◽  
Jia-Xin Chen
Keyword(s):  
Bandwidth Adaptation ◽  
Tracking Object

scholarly journals Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1245 ◽  
Author(s):  
Tao Wang ◽  
Wen Wang ◽  
Hui Liu ◽  
Tianping Li
Keyword(s):  
Big Data ◽  
Particle Filter ◽  
Skin Color ◽  
Feature Fusion ◽  
Average Distance ◽  
Histogram Method ◽  
Face Features ◽  
The Face ◽  
Edge Features ◽  
Tracking Object

With the revolutionary development of cloud computing and internet of things, the integration and utilization of “big data” resources is a hot topic of the artificial intelligence research. Face recognition technology information has the advantages of being non-replicable, non-stealing, simple and intuitive. Video face tracking in the context of big data has become an important research hotspot in the field of information security. In this paper, a multi-feature fusion adaptive adjustment target tracking window and an adaptive update template particle filter tracking framework algorithm are proposed. Firstly, the skin color and edge features of the face are extracted in the video sequence. The weighted color histogram are extracted which describes the face features. Then we use the integral histogram method to simplify the histogram calculation of the particles. Finally, according to the change of the average distance, the tracking window is adjusted to accurately track the tracking object. At the same time, the algorithm can adaptively update the tracking template which improves the accuracy and accuracy of the tracking. The experimental results show that the proposed method improves the tracking effect and has strong robustness in complex backgrounds such as skin color, illumination changes and face occlusion.

Human Motion Capture Driven by Orientation Measurements

1999 ◽  
Vol 8 (2) ◽  
pp. 187-203 ◽  
Author(s):  
Tom Molet ◽  
Ronan Boulic ◽  
Daniel Thalmann
Keyword(s):  
Motion Capture ◽  
Human Motion ◽  
High Rate ◽  
Position Measurement ◽  
Human Motion Capture ◽  
Fast Tracking ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Tracking Object ◽  
Capture Techniques

Motion-capture techniques are rarely based on orientation measurements for two main reasons: (1) optical motion-capture systems are designed for tracking object position rather than their orientation (which can be deduced from several trackers), (2) known animation techniques, like inverse kinematics or geometric algorithms, require position targets constantly, but orientation inputs only occasionally. We propose a complete human motion-capture technique based essentially on orientation measurements. The position measurement is used only for recovering the global position of the performer. This method allows fast tracking of human gestures for interactive applications as well as high rate recording. Several motion-capture optimizations, including the multijoint technique, improve the posture realism. This work is well suited for magnetic-based systems that rely more on orientation registration (in our environment) than position measurements that necessitate difficult system calibration.

Intelligent Bandwidth Allocation of IPTV Streams with Bitstream Complexity Measures

2015 ◽  
pp. 1941-1961
Author(s):  
Sandro Moiron ◽  
Rouzbeh Razavi ◽  
Martin Fleury ◽  
Mohammed Ghanbari
Keyword(s):  
Bandwidth Allocation ◽  
Video Quality ◽  
Access Network ◽  
Temporal Coding ◽  
Wireless Access Networks ◽  
Core Network ◽  
Complexity Measures ◽  
Broadband Wireless ◽  
Logic Control ◽  
Bandwidth Adaptation

IPTV video services are increasingly being considered for delivery to mobile devices over broadband wireless access networks. The IPTV streams or channels are multiplexed together for transport across an IP core network prior to distribution across the access network. According to the type of access network, prior bandwidth constraints exist that restrict the multiplex data-rate. This paper presents a bandwidth allocation scheme based on content complexity to equalize the overall video quality of the IPTV sub-streams, in effect a form of statistical multiplexing. Bandwidth adaptation is achieved through a bank of bit-rate transcoders. Complexity metrics serve to estimate the appropriate bandwidth share for each stream, prior to distribution over a wireless or ADSL access network. These metrics are derived after entropy decoding of the input compressed bit-streams, without the delay resulting from a full decode. Fuzzy-logic control serves to adjust the balance between spatial and temporal coding complexity. The paper examines constant and varying bandwidth scenarios. Experimental results show a significant overall gain in video quality in comparison to a fixed bandwidth allocation.

A Growing Neural Gas Approach to Classify Vehicles in Traffic Environments

2017 ◽  
Vol 7 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Miguel A. Molina-Cabello ◽  
Rafael Marcos Luque-Baena ◽  
Ezequiel López-Rubio ◽  
Juan Miguel Ortiz-de-Lazcano-Lobato ◽  
Enrique Domínguez
Keyword(s):  
Video Surveillance ◽  
Extraction Process ◽  
Traffic Monitoring ◽  
Point Of View ◽  
Growing Neural Gas ◽  
Feature Extraction Method ◽  
Vehicle Type ◽  
Neural Gas ◽  
Detection And Tracking ◽  
Tracking Object

Automated video surveillance presents a great amount of applications and one of them is traffic monitoring. Vehicle type detection can provide information about the characteristics of the traffic flow to human traffic controllers in order to facilitate their decision-making process. A video surveillance system is proposed in this work to execute such classification. First of all, a foreground detection and tracking object process has been carried out. Once the vehicles are detected, a feature extraction method obtains the most significant features of this detected vehicles. When the extraction process is done, the vehicle types are determined by employing a set of Growing Neural Gas neural networks. The performance of the proposal has been analyzed from a qualitative and quantitative point of view by using a set of benchmark traffic video sequences, with acceptable results.

scholarly journals Estimation for Motion in Tracking and Detection Objects with Kalman Filter

2020 ◽  
Author(s):  
Afef Salhi ◽  
Fahmi Ghozzi ◽  
Ahmed Fakhfakh
Keyword(s):  
Computer Vision ◽  
Kalman Filter ◽  
Visual Motion ◽  
Optimal Solution ◽  
Block Matching ◽  
Detection And Tracking ◽  
Tracking Object ◽  
Tracking Objects ◽  
Localization And Tracking ◽  
Prediction Parameters

The Kalman filter has long been regarded as the optimal solution to many applications in computer vision for example the tracking objects, prediction and correction tasks. Its use in the analysis of visual motion has been documented frequently, we can use in computer vision and open cv in different applications in reality for example robotics, military image and video, medical applications, security in public and privacy society, etc. In this paper, we investigate the implementation of a Matlab code for a Kalman Filter using three algorithm for tracking and detection objects in video sequences (block-matching (Motion Estimation) and Camshift Meanshift (localization, detection and tracking object)). The Kalman filter is presented in three steps: prediction, estimation (correction) and update. The first step is a prediction for the parameters of the tracking and detection objects. The second step is a correction and estimation of the prediction parameters. The important application in Kalman filter is the localization and tracking mono-objects and multi-objects are given in results. This works presents the extension of an integrated modeling and simulation tool for the tracking and detection objects in computer vision described at different models of algorithms in implementation systems.

Sign in / Sign up

Export Citation Format

Share Document