S. Shailesh

Bio: S. Shailesh is an academic researcher from Cochin University of Science and Technology. The author has contributed to research in topics: Dance & Computer science. The author has an hindex of 2, co-authored 2 publications receiving 7 citations.

Automatic annotation of dance videos based on foot postures

Abstract: Along with the advancements in the field of artificial intelligence, machine learning, and deep learning video annotation has become a more exciting and crucial problem, especially the video from medical, arts performance games and so on. In India, there is an increased demand for digitizing and archiving of arts and culture . This paper focuses on annotating dance videos based on foot postures (stanas) in an automatic manner. Using transfer-learning, the features from the images are extracted and a deep neural network is used for image classification. A trained model of Deep Stana Classifier is used for identifying stanas from the frames of a video. A complete annotation system comprises of a coupled architecture, one Deep Stana Classifier module, and an annotation module. The findings on the accuracy obtained for the Deep Stana Classifier produced positive results. In the second phase, the result of the annotations made on the video is kept as an index structure in JSON object format. The scope and opportunity of this work in the future is consistent and useful for annotating dance videos as well as videos from another domain.

Cultural heritage preservation through dance digitization: A review

Abstract: ‘Cultural heritage conservation’ encompasses all actions and measures taken towards the life of cultural heritage while strengthening the long-term preservation of its messages and values. It has acquired significant heedfulness in recent years due to its wide applications in the potential research fields of image analysis, machine intelligence, computer vision, and pattern recognition. Cultural heritage preservation comprises both tangible and intangible resources. A significant part of intangible resources constitutes performing art such as dance or music. The era of digitization made way for the digitized form of heritage artifacts, which helps preserve knowledge. Many researchers have developed various approaches to automate the dance, identify the gesture, poses, and stance (Pose Recognition), recognize the dance forms, dance movement classification, etc., with impressive achievements. We present a comprehensive view of approaches proposed in the various fields of computerized dance modeling that aid in cultural heritage preservation.

Understanding dance semantics using spatio-temporal features coupled GRU networks

Abstract: • Designed a framework for understanding dance semantics from live videos which can be used for real-time annotation. • Novel method is proposed to handle the spatio-temporal dynamics of the dance using key-points coupled with GRU networks. • A key point normalization method is proposed to handle spatial dependency and translation variance. • Created a dance video repository by recording live performances of dancers and videos from the internet. The efforts are taken for cultural heritage preservation lead to many digitization initiatives. Indian Classical Dance and its tradition has proven historical importance. Computer-aided archiving and preservation of Indian classical dance resources open enormous opportunities for computational analysis. With the help of recent computational advancements in the field of Computer Vision, these archives can be transformed into intelligent information retrieval systems. In this work, we propose a novel method for understanding the dance semantics by making use of the Spatio-temporal variations of dance features. A video archive is created as a part of this work, from live recordings of different trained dancers and clippings from the internet. The Deep Pose Estimator coupled GRU Model deals with the spatial aspects with a deep learning pose estimator and handles the temporal perspective with GRU Network. The efficiency of the proposed method was compared with benchmark methods such as A 3D-Convolutional Neural Network-based Model, Time Distributed CNN-LSTM Model, and Hybrid Transfer Learning - LSTM Model and the results show the proposed method outperformed others even with different video resolutions.

A novel neural network model with distributed evolutionary approach for big data classification

Abstract: Abstract The considerable improvement of technology produced for various applications has resulted in a growth in data sizes, such as healthcare data, which is renowned for having a large number of variables and data samples. Artificial neural networks (ANN) have demonstrated adaptability and effectiveness in classification, regression, and function approximation tasks. ANN is used extensively in function approximation, prediction, and classification. Irrespective of the task, ANN learns from the data by adjusting the edge weights to minimize the error between the actual and predicted values. Back Propagation is the most frequent learning technique that is used to learn the weights of ANN. However, this approach is prone to the problem of sluggish convergence, which is especially problematic in the case of Big Data. In this paper, we propose a Distributed Genetic Algorithm based ANN Learning Algorithm for addressing challenges associated with ANN learning for Big data. Genetic Algorithm is one of the well-utilized bio-inspired combinatorial optimization methods. Also, it is possible to parallelize it at multiple stages, and this may be done in an extremely effective manner for the distributed learning process. The proposed model is tested with various datasets to evaluate its realizability and efficiency. The results obtained from the experiments show that after a specific volume of data, the proposed learning method outperformed the traditional methods in terms of convergence time and accuracy. The proposed model outperformed the traditional model by almost 80% improvement in computational time.

Yoga Asana Identification: A Deep Learning Approach

Abstract: Yoga is a healthy practice that originated from India, to rejuvenate a man in his physical, mental, and spiritual wellness. Moving with the brisk technology advancements, there is a vast opportunity for computational probing in all social domains. But still, the utilization of artificial intelligence and machine learning techniques for applying to an interdisciplinary domain like yoga is quite challenging. In this work, a system that recognizes a yoga posture from an image or a frame of a video has been developed with the help of deep learning techniques like convolutional neural networks (CNN) and transfer learning. We have considered images of 10 different asanas for training the model as well as evaluating the prediction accuracy. The prediction model backed with transfer learning shows promising results with 85% prediction accuracy and this system can be considered as an initial step to build an automated yoga image and video analysis tool.

An Extensive Review of Computational Dance Automation Techniques and Applications

Abstract: Dance is an art and when technology meets this kind of art, it's a novel attempt in itself. Several researchers have attempted to automate several aspects of dance, right from dance notation to choreography. Furthermore, we have encountered several applications of dance automation like e-learning, heritage preservation, etc. Despite several attempts by researchers for more than two decades in various styles of dance all round the world, we found a review paper that portrays the research status in this area dating to 1990 \cite{politis1990computers}. Hence, we decide to come up with a comprehensive review article that showcases several aspects of dance automation. This paper is an attempt to review research work reported in the literature, categorize and group all research work completed so far in the field of automating dance. We have explicitly identified six major categories corresponding to the use of computers in dance automation namely dance representation, dance capturing, dance semantics, dance generation, dance processing approaches and applications of dance automation systems. We classified several research papers under these categories according to their research approach and functionality. With the help of proposed categories and subcategories one can easily determine the state of research and the new avenues left for exploration in the field of dance automation.

An extensive review of computational dance automation techniques and applications

Abstract: Dance is an art and when technology meets this kind of art, it is a novel attempt in itself. Many researchers have attempted to automate several aspects of dance, right from dance notation to chore...

Posture and sequence recognition for Bharatanatyam dance performances using machine learning approaches

Abstract: Understanding the underlying semantics of performing arts like dance is a challenging task. Analysis of dance is useful to preserve cultural heritage, make video recommendation systems, and build tutoring systems. To create such a dance analysis application, three aspects of dance analysis must be addressed: (1) segment the dance video to find representative action elements, (2) recognize the detected action elements, and (3) recognize sequences formed by combining action elements according to specific rules. This paper attempts to address the three fundamental problems of dance analysis raised above, with a focus on Indian Classical Dance, em Bharatanatyam. Since dance is driven by music, we use both musical and motion information to extract action elements. The action elements are then recognized using machine learning and deep learning techniques. Finally, the Hidden Markov Model (HMM) and Long Short-Term Memory (LSTM) are used to recognize the dance sequence.

Cultural heritage preservation through dance digitization: A review

Abstract: ‘Cultural heritage conservation’ encompasses all actions and measures taken towards the life of cultural heritage while strengthening the long-term preservation of its messages and values. It has acquired significant heedfulness in recent years due to its wide applications in the potential research fields of image analysis, machine intelligence, computer vision, and pattern recognition. Cultural heritage preservation comprises both tangible and intangible resources. A significant part of intangible resources constitutes performing art such as dance or music. The era of digitization made way for the digitized form of heritage artifacts, which helps preserve knowledge. Many researchers have developed various approaches to automate the dance, identify the gesture, poses, and stance (Pose Recognition), recognize the dance forms, dance movement classification, etc., with impressive achievements. We present a comprehensive view of approaches proposed in the various fields of computerized dance modeling that aid in cultural heritage preservation.