scholarly journals Background Subtraction of an Indian Classical Dance Videos using Adaptive Temporal Averaging Method

2021 ◽  
Vol 10 (3) ◽  
pp. 16-19
Keyword(s):  
Gaussian Distribution ◽  
Background Subtraction ◽  
Moving Objects ◽  
Averaging Method ◽  
Quality Parameters ◽  
Detection Methods ◽  
Classical Dance ◽  
Temporal Averaging ◽  
Update Rate ◽  
Indian Classical Dance

A slew of motion detection methods have been proposed in recent years. The background includes some constraints such as changes in illumination, shadow, cluttered the background, scene change and speed of dance between hand gestures and body gestures are different. One of the most basic methods for background subtraction is temporal averaging. We looked at a new adaptive temporal averaging approach in this paper. To identify moving objects in video sequences, an adaptive temporal averaging technique is used. Depending upon the speed of the technique we proposed a Gaussian distribution technique. Gaussian distribution done background subtraction depending upon active pixels it differentiates whether it is a background or foreground. The background model's update rate has been modified to be adaptive and determined by pixel difference .Our aim is to improve the method's F-measure by making it more adaptable to various scene scenarios. The experiment results are shown and evaluated. The proposed method and the original method's quality parameters are compared

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

Algorithm of Color Detection for Moving Video Objects Based on Mode Matching

2012 ◽  
Vol 239-240 ◽  
pp. 1000-1003
Author(s):  
Zhao Quan Cai ◽  
Hui Hu ◽  
Tao Xu ◽  
Wei Luo ◽  
Yi Cheng He
Keyword(s):  
Background Subtraction ◽  
Moving Objects ◽  
Color Matching ◽  
Human Beings ◽  
Current Image ◽  
Color Recognition ◽  
Recognition Ability ◽  
Color Identification ◽  
The Difference ◽  
Background Image

It is urgent to study how to effectively identify color of moving objects from the video in the information era. In this paper, we present the color identification methods for moving objects on fixed camera. One kind of the methods is background subtraction that recognizes the foreground objects by compare the difference of pixel luminance between the current image and the background image at the same coordinates. Another kind is based on the statistics of HSV color and color matching which makes the detection more similar to the color identification of the human beings. According to the experiment results, after the completion of the background modelling, our algorithm of background subtraction, statistics of the HSV color and the color matching have strong color recognition ability on the moving objects of video.

Survey of Musculoskeletal Injuries among Female Bharatanatyam Dancers in the Udupi District of India

2021 ◽  
Vol 36 (3) ◽  
pp. 199-206
Author(s):  
Lavanya P Kumar ◽  
Shruti J Shenoy
Keyword(s):  
Medical Professional ◽  
Musculoskeletal Injuries ◽  
Limited Information ◽  
Classical Dance ◽  
Dance Training ◽  
Lower Back ◽  
Injury Occurrence ◽  
Indian Classical Dance ◽  
The Impact ◽  
And Training

BACKGROUND: Bharatanatyam is an Indian classical dance form that is practiced globally. There is limited information about the prevalence of injuries in Bharatanatyam dancers. OBJECTIVES: To investigate the prevalence of musculoskeletal injuries and specifics of dance training in female Bharatanatyam dancers in the Udupi district of India. METHODS: We developed and tested a survey for Bharatanatyam dancers regarding injury history in the prior year, including location, time loss, cause, and need for medical help. We also obtained demographic and training information. RESULTS: 101 dancers completed the survey. 10.8% of dancers reported musculoskeletal injuries because of participation in dance. They sustained 0.65 injuries/1,000 hours of dancing. The most frequently injured areas were ankle (27.2%) and knee (27.2%) followed by lower back (13.6%) and hip (9%). Despite being injured, 36.4% of the dancers continued to dance. 54.5% of the injured dancers sought the help of a medical professional for their dance-related injuries. The most common surface for dance was concrete followed by other hard surfaces such as marble and tile. CONCLUSION: Female Bharatanatyam dancers are prone to injuries of the lower extremity and back. Most dancers in our study practice the Pandanalluru style on hard surfaces. There is a need to investigate the impact of training factors on the injury occurrence.

scholarly journals Indian Classical Dance Classification with Adaboost Multiclass Classifier on Multifeature Fusion

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
K. V. V. Kumar ◽  
P. V. V. Kishore ◽  
D. Anil Kumar
Keyword(s):  
Feature Fusion ◽  
Discrete Wavelet ◽  
Classical Dance ◽  
Video Frames ◽  
Shape Signature ◽  
Human Movements ◽  
Hu Moments ◽  
Interesting Task ◽  
Indian Classical Dance ◽  
Multiclass Classifier

Extracting and recognizing complex human movements from unconstraint online video sequence is an interesting task. In this paper the complicated problem from the class is approached using unconstraint video sequences belonging to Indian classical dance forms. A new segmentation model is developed using discrete wavelet transform and local binary pattern (LBP) features for segmentation. A 2D point cloud is created from the local human shape changes in subsequent video frames. The classifier is fed with 5 types of features calculated from Zernike moments, Hu moments, shape signature, LBP features, and Haar features. We also explore multiple feature fusion models with early fusion during segmentation stage and late fusion after segmentation for improving the classification process. The extracted features input the Adaboost multiclass classifier with labels from the corresponding song (tala). We test the classifier on online dance videos and on an Indian classical dance dataset prepared in our lab. The algorithms were tested for accuracy and correctness in identifying the dance postures.

Technological Mediation in Odissi Dance

2015 ◽  
pp. 474-491
Author(s):  
Shreelina Ghosh
Keyword(s):  
Qualitative Research ◽  
Digital Technologies ◽  
Classroom Community ◽  
Traditional Values ◽  
Online Instructors ◽  
Classical Dance ◽  
Dance Teachers ◽  
Online Videos ◽  
Virtual Class ◽  
Indian Classical Dance

The practice of teaching in an online composition class might potentially eliminate interpersonal interactivity in a classroom community. Digital mediation can be problematic for functional collaboration in a virtual class. The problem that online instructors might face is one that some traditional Odissi dance teachers also experience. In order to explore the conflict between tradition and mediations with technology, this study focuses on Odissi, an Indian classical dance, and examines how digital technologies of teaching, like CDs, DVD, online videos, and synchronous videos, are transforming the practice and teaching of this traditional dance. A qualitative research of the field of Odissi dance revealed that technologizing the dance might be unavoidable, but to some practitioners it may be disrupting Odissi's traditional values. This chapter reasserts the position of the teacher in an online pedagogic space and argues that the presence or simulated presence of bodies might be vital in learning and composing collaboratively.

Object-Based Surveillance Video Synopsis Using Genetic Algorithm

2017 ◽  
pp. 193-219
Author(s):  
Shefali Gandhi ◽  
Tushar V. Ratanpara
Keyword(s):  
Genetic Algorithm ◽  
Background Subtraction ◽  
Moving Objects ◽  
Time Lapse ◽  
Original Video ◽  
Surveillance Video ◽  
Time Activity ◽  
Video Synopsis ◽  
Object Based ◽  
Poisson Image Editing

Video synopsis provides representation of the long surveillance video, while preserving the essential activities of the original video. The activity in the original video is covered into a shorter period by simultaneously displaying multiple activities, which originally occurred at different time segments. As activities are to be displayed in different time segments than original video, the process begins with extracting moving objects. Temporal median algorithm is used to model background and foreground objects are detected using background subtraction method. Each moving object is represented as a space-time activity tube in the video. The concept of genetic algorithm is used for optimized temporal shifting of activity tubes. The temporal arrangement of tubes which results in minimum collision and maintains chronological order of events is considered as the best solution. The time-lapse background video is generated next, which is used as background for the synopsis video. Finally, the activity tubes are stitched on the time-lapse background video using Poisson image editing.

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