Robust detection and tracking of human faces with an active camera

2002 ◽  
Author(s):  
D. Comaniciu ◽  
V. Ramesh
Keyword(s):  
Robust Detection ◽  
Detection And Tracking ◽  
Human Faces ◽  
Active Camera

TRACKING HUMAN MOTION FROM MONOCULAR SEQUENCES

2008 ◽  
Vol 08 (03) ◽  
pp. 455-471 ◽  
Author(s):  
LAURO SNIDARO ◽  
GIAN LUCA FORESTI ◽  
LUCA CHITTARO
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Digital Camera ◽  
Human Motion ◽  
Movie Industry ◽  
Sources Of Information ◽  
Robust Detection ◽  
Detection And Tracking ◽  
Novel Approach ◽  
Body Joints

In recent years, analysis of human motion has become an increasingly relevant research topic with applications as diverse as animation, virtual reality, security, and advanced human-machine interfaces. In particular, motion capture systems are well known nowadays since they are used in the movie industry. These systems require expensive multi-camera setups or markers to be worn by the user. This paper describes an attempt to provide a markerless low cost and real-time solution for home users. We propose a novel approach for robust detection and tracking of the user's body joints that exploits different algorithms as different sources of information and fuses their estimates with particle filters. This system may be employed for real-time animation of VRML or X3D avatars using an off-the-shelf digital camera and a standard PC.

scholarly journals Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning

Geophysics ◽  
2015 ◽  
Vol 80 (6) ◽  
pp. WD101-WD116 ◽  
Author(s):  
Zhen Wang ◽  
Tamir Hegazy ◽  
Zhiling Long ◽  
Ghassan AlRegib
Keyword(s):  
Subspace Learning ◽  
Robust Detection ◽  
Salt Domes ◽  
Detection And Tracking ◽  
Noise Robust

scholarly journals An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

2020 ◽  
Vol 4 (4) ◽  
pp. 27
Author(s):  
Liang Cheng Chang ◽  
Shreya Pare ◽  
Mahendra Singh Meena ◽  
Deepak Jain ◽  
Dong Lin Li ◽  
...  
Keyword(s):  
Particle Filtering ◽  
Tracking System ◽  
Human Detection ◽  
Surveillance Systems ◽  
Camera Control ◽  
Detection And Tracking ◽  
Filter Approach ◽  
And Performance ◽  
The Stability ◽  
Active Camera

At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.

A new database for Intense Mediterranean Cyclones 

2021 ◽  
Author(s):  
M. Carmen Alvarez-Castro ◽  
David Gallego ◽  
Pedro Ribera ◽  
Cristina Peña-Ortiz ◽  
Davide Faranda
Keyword(s):  
Relative Vorticity ◽  
Sea Level Pressure ◽  
Robust Detection ◽  
Detection Algorithms ◽  
Mediterranean Cyclones ◽  
Detection And Tracking ◽  
Detection Approach ◽  
Local Extrema ◽  
Atmospheric Mass ◽  
Selection Of

<p>To better assess the future risks associated with Intense Mediterranean Cyclones (IMC) a better understanding of their features, variability, frequency and intensity is required, including a robust detection method. The application of different detection algorithms provides results that are remarkably similar in some aspects but may be very different in others even using the same data. Thus, the selection of a particular method can significantly affect the results. For these reasons it is necessary to use different approaches and datasets to study the sensitivity and robustness of the detection approach. Those approaches often use minima in sea-level pressure (SLP) or extrema in relative vorticity or both to first identify the eye of the cyclone. SLP reflects the atmospheric mass distribution, and is representative of synoptic-scale atmospheric processes. On the other hand, the relative vorticity displays higher variability and is representative of the atmospheric circulation, being able to detect several local extrema (more than one centre), it can reduce uncertainties in the cyclone detection and tracking.</p><p>Therefore, within the framework of the EFIMERA project and to detect and track IMC we use a combination of different methods based on previous studies found in the literature. This new list of detected IMC events, together with the observed and well documented ones, are used here to create a new IMC database to be used for the study of their impacts and risk associated.</p>

Deep Learning for Moving Object Detection and Tracking

2021 ◽  
pp. 136-163
Author(s):  
Kalirajan K. ◽  
Seethalakshmi V. ◽  
Venugopal D. ◽  
Balaji K.
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Model ◽  
Moving Object Detection ◽  
Moving Object ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
High Level ◽  
Human Faces ◽  
Deep Learning Model

Moving object detection and tracking is the process of identifying and locating the class objects such as people, vehicle, toy, and human faces in the video sequences more precisely without background disturbances. It is the first and foremost step in any kind of video analytics applications, and it is greatly influencing the high-level abstractions such as classification and tracking. Traditional methods are easily affected by the background disturbances and achieve poor results. With the advent of deep learning, it is possible to improve the results with high level features. The deep learning model helps to get more useful insights about the events in the real world. This chapter introduces the deep convolutional neural network and reviews the deep learning models used for moving object detection. This chapter also discusses the parameters involved and metrics used to assess the performance of moving object detection in deep learning model. Finally, the chapter is concluded with possible recommendations for the benefit of research community.

scholarly journals Robust detection and tracking of annotations for outdoor augmented reality browsing

2011 ◽  
Vol 35 (4) ◽  
pp. 831-840 ◽  
Author(s):  
Tobias Langlotz ◽  
Claus Degendorfer ◽  
Alessandro Mulloni ◽  
Gerhard Schall ◽  
Gerhard Reitmayr ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Robust Detection ◽  
Detection And Tracking ◽  
Outdoor Augmented Reality
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