scholarly journals A Novel Changing Athlete Body Real-Time Visual Tracking Algorithm Based on Distractor-Aware SiamRPN and HOG-SVM

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 378 ◽  
Author(s):  
Mingwei Sheng ◽  
Weizhe Wang ◽  
Hongde Qin ◽  
Lei Wan ◽  
Jun Li ◽  
...  
Keyword(s):  
Real Time ◽  
Large Data ◽  
Video Frame ◽  
Support Vector ◽  
Key Frame ◽  
Histograms Of Oriented Gradients ◽  
Time Operation ◽  
Haar Feature ◽  
Real Time Operation ◽  
Detection Window

Athlete detection in sports videos is a challenging task due to the dynamic and cluttered background. Distractor-aware SiamRPN (DaSiamRPN) has a simple network structure and can be utilized to perform long-term tracking of large data sets. However, similarly to the Siamese network, the tracking results heavily rely on the given position in the initial frame. Hence, there is a lack of solutions for some complex tracking scenarios, such as running and changing of bodies of athletes, especially in the stage from squatting to standing to running. The Haar feature-based cascade classifier is involved to catch the key frame, representing the video frame of the most dramatic changes of the athletes. DaSiamRPN is implemented as the tracking method. In each frame after the key frame, a detection window is given based on the bounding box generated by the DaSiamRPN tracker. In the new detection window, a fusion method (HOG-SVM) combining features of Histograms of Oriented Gradients (HOG) and a linear Support-Vector Machine (SVM) is proposed for detecting the athlete, and the tracking results are updated in real-time by fusing the tracking results of DaSiamRPN and HOG-SVM. Our proposed method has reached a stable and accurate tracking effect in testing on men’s 100 m video sequences and has realized real-time operation.

SVM-BASED PHONEME CLASSIFICATION AND LIP SHAPE REFINEMENT IN REAL-TIME LIP-SYNCH SYSTEM

2006 ◽  
Vol 20 (07) ◽  
pp. 1029-1051 ◽  
Author(s):  
HANSEOK KO ◽  
DAVID K. HAN
Keyword(s):  
Real Time ◽  
Processing Time ◽  
Support Vector ◽  
Mel Frequency Cepstral Coefficients ◽  
Phoneme Classification ◽  
Second Stage ◽  
Single Frame ◽  
Time Operation ◽  
Real Time Operation ◽  
Coarse To Fine

In this paper, we present a real time lip-synch system that activates 2-D avatar's lip motion in synch with incoming speech utterance. To achieve the real time operation of the system, the processing time was minimized by "merge and split" procedures resulting in coarse-to-fine phoneme classification. At each stage of phoneme classification, the support vector machine (SVM) method was applied to reduce the computational load while maintaining the desired accuracy. The coarse-to-fine phoneme classification, is accomplished via two_stages of feature extraction: in the first stage, each speech frame is acoustically analyzed for three classes of lip opening using Mel Frequency Cepstral Coefficients (MFCC) as a feature; in the second stage, each frame is further refined for detailed lip shape using formant information. The method was implemented in 2-D lip animation and it was demonstrated that the system was effective in accomplishing real-time lip-synch. This approach was tested on a PC using the Microsoft Visual Studio with an Intel Pentium IV 1.4 Giga Hz CPU and 384 MB RAM. It was observed that the methods of phoneme merging and SVM achieved about twice the speed in recognition than the method employing the Hidden Markov Model (HMM). A typical latency time per a single frame observed using the proposed method was in the order of 18.22 milliseconds while an HMM method under identical conditions resulted about 30.67 milliseconds.

scholarly journals Robust hand gesture recognition using multiple shape-oriented visual cues

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Depth Map ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Hand Pose Estimation ◽  
Time Operation ◽  
Hand Pose

AbstractRobust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.

Real-Time Operation and Characterization of a High-Performance Time-Based Accelerometer

2015 ◽  
Vol 24 (6) ◽  
pp. 1703-1711 ◽  
Author(s):  
Rosana Alves Dias ◽  
Filipe Serra Alves ◽  
Margaret Costa ◽  
Helder Fonseca ◽  
Jorge Cabral ◽  
...  
Keyword(s):  
Real Time ◽  
High Performance ◽  
Performance Time ◽  
Time Operation ◽  
Real Time Operation

Cultural Change in ALS Real Time Operation and Optimization in Vaca Muerta Through CWE

2018 ◽  
Author(s):  
J. I. Alvarez Claramunt ◽  
P. E. Bizzotto ◽  
F. Sapag ◽  
E. Ferrigno ◽  
J. L. Barros ◽  
...  
Keyword(s):  
Real Time ◽  
Cultural Change ◽  
Time Operation ◽  
Vaca Muerta ◽  
Real Time Operation

scholarly journals TDOA-based microwave imaging algorithm for real-time microwave ablation monitoring

2017 ◽  
Vol 10 (2) ◽  
pp. 169-178 ◽  
Author(s):  
Shouhei Kidera ◽  
Luz Maria Neira ◽  
Barry D. Van Veen ◽  
Susan C. Hagness
Keyword(s):  
Real Time ◽  
Microwave Ablation ◽  
Microwave Imaging ◽  
Ablation Zone ◽  
Real Time Monitoring ◽  
Imaging Algorithm ◽  
Array Measurements ◽  
Time Operation ◽  
Real Time Operation ◽  
Propagation Media

Microwave ablation is widely recognized as a promising minimally invasive tool for treating cancer. Real-time monitoring of the dimensions of the ablation zone is indispensable for ensuring an effective and safe treatment. In this paper, we propose a microwave imaging algorithm for monitoring the evolution of the ablation zone. Our proposed algorithm determines the boundary of the ablation zone by exploiting the time difference of arrival (TDOA) between signals received before and during the ablation at external antennas surrounding the tissue, using the interstitial ablation antenna as the transmitter. A significant advantage of this method is that it requires few assumptions about the dielectric properties of the propagation media. Also the simplicity of the signal processing, wherein the TDOA is determined from a cross-correlation calculation, allows real-time monitoring and provides robust performance in the presence of noise. We investigate the performance of this approach for the application of breast tumor ablation. We use simulated array measurements obtained from finite-difference time-domain simulations of magnetic resonance imaging-derived numerical breast phantoms. The results demonstrate that our proposed method offers the potential to achieve millimeter-order accuracy and real-time operation in estimating the boundary of the ablation zone in heterogeneous and dispersive breast tissue.

scholarly journals CONTROLLING AND MONITORING OF AUTOMATION WATER SUPPLY SYSTEM BASED ON IOT WITH THEFT IDENTIFICATION

2017 ◽  
Vol 5 (5) ◽  
pp. 320-325
Author(s):  
Ahmad T. Jaiad ◽  
Hamzah Sabr Ghayyib
Keyword(s):  
Water Supply ◽  
Real Time ◽  
Water Conservation ◽  
Water Distribution ◽  
Network Connectivity ◽  
Raspberry Pi ◽  
Human Beings ◽  
Residential Areas ◽  
Time Operation ◽  
Real Time Operation

Water is the most precious and valuable because it’s a basic need of all the human beings but, now a day water supply department are facing problem in real time operation this is because less amount of water in resources due to less rain fall. With increase in Population, urban residential areas have increased because of this reasons water has become a crucial problem which affects the problem of water distribution, interrupted water supply, water conservation, water consumption and also the water quality so, to overcome water supply related problems and make system efficient there is need of proper monitoring and controlling system. In this project, we are focusing on continuous and real time monitoring of water supply in IOT platform. Water supply with continuous monitoring makes a proper distribution so that, we can have a record of available amount of water in tanks, flow rate, abnormality in distribution line. Internet of things is nothing but the network of physical objects embedded with electronics, sensors, software, and network connectivity. Monitoring can be done from anywhere as central office. Using Adafruit as free sever data continuously pushed on cloud so we can see data in real time operation. Using different sensors with controller and raspberry pi as Mini computer can monitor data and also control operation from cloud with efficient client server communication.

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