Tchebichef moments and the PCA technique are consistently superior and they are more robust features for view invariant posture classification.
This research work is motivated towards developing CBA systems like content-based annotation and re- trieval of Bharatanatyam dance videos. As a first step, the DOM based hand posture recognition technique developed in Chapter 4 is applied for robust recognition of the Asamyuta hastas in Bharatanatyam. This chap- ter presents in detail the development of the Asamyuta hasta database, the system implementation strategies and the experimental studies on the automatic recognition of hand postures constituting the Asamyuta hastas in Bharatanatyam.
5.1 Introduction
Dance is a remarkable art form that involves body movements and facial expressions to portray human emotions insync with the music. The intelligence level in a dance form can be either high so as to depict the vocal information or subtle as just movements in accord with the rhythm. The artistic features in the dance genre offer insight into the ethnicity, geography, dress, and the religious nature of a particular populace [184].
With the effort to conserve and pass on the culture, the dance styles are documented using the notation systems.
These notation systems are symbolic representations of movement that are used for individual interpretation and learning [185]. The widely employed dance notation system is the Labanotation [185–187].
The first instance of technology in dance is the use of computers to compose and edit the dance notation scores. Eventually, the developments in the computer and the imaging technologies have facilitated CBA systems in dance. These systems include dance partner robots [188], interactive dance games [189], automated dance training and evaluation systems [190, 191], dance synthesis [192], and dancing avatar animation [193].
A few works [194–199] have concentrated in developing computer vision based markerless motion-capture methods for dance technology. Some of the dance forms for which vision based gesture representation algo- rithms are explored include the modern or free style dance [190, 194, 197, 200, 201], ballet [198, 202, 203], ball- room dance [188] and Japanese traditional dances [204–206]. Other applications of computer vision techniques for dance includes retrieval systems [207,208] and dance video annotations [209–211]. However, only very few works have concentrated on developing intelligent algorithms for Indian classical dances like Bharatanatyam.
Mamania et al. [195], have used some basic movements in Bharatanatyam for their work on developing mark- erless motion-capture method from monocular videos. In [212], Bharatanatyam is considered for developing concept based video annotation. The technique relied on specific body movements, body postures and mu- sic for annotation. Recently, vision based techniques for recognising the hand postures in Bharatanatyam are developed. In [213], edge orientation histograms were employed as features for representing the single-hand
5.1 Introduction
postures in Bharatanatyam. Their work is aimed at facilitating E-learning tools for Bharatanatyam. Similar technique was used in [214] for recognising the two-hand postures in Bharatanatyam. Their work combined the edge orientation histogram features and the skeleton based matching technique for classifying the hand postures.
From the literature, it is evident that the CBA systems are yet to be adopted to different classical dance genres around the world. Particularly, the Indian classical dance forms are yet to advance even to the level of automated notation systems. The Indian classical dance, Bharatanatyam is an intricate dance form that comprises of hand postures, facial expressions and different movements with respect to each part of the body.
Hence, developing CBA systems for Bharatanatyam is a challenge.
The integral meaning of a Bharatanatyam dance performance is conveyed through the hand postures. Un- like the simple hand postures that involve basic movements like abduction/adduction and extension/flexion of fingers, the hand postures in Bharatanatyam involve complex movements in which the configuration at every finger joint varies resulting in variegated hand postures. Therefore, for successful realisation of a vision based CBA system for Bharatanatyam, it is crucial to develop image processing techniques for efficient description and classification of the hand postures in Bharatanatyam. It is also essential that the technique must be robust to the user and the view-angle variations.
In a Bharatanatyam dance video, the frames containing the hand postures will be considered as the key frames. The key frames and the order in which these frames occur within the shots can be used to characterise a video segment. Eventually, the descriptions of the shots will represent the entire video. Thus, it can be under- stood that the primary factor in developing a vision-based CBA system for Bharatanatyam is the recognition of the hand postures in the key frames. Some of the major issues in developing the vision-based CBA system for recognizing the hand postures in a Bharatanatyam dance video are:
(i) Segmentation of the hand from the dance video.
(ii) Variations in the scale, the orientation and the spatial position of the hand postures.
(iii) Structural variations due to variabilities in the hand geometry and the gesturing style of the dancers.
(iv) Structural distortions due to varying view-angles that occur while capturing the dance video.
This research is focussed towards developing techniques that are robust to variations in the shape of the hand posture caused by user and viewpoint changes.
In Chapter 4, a DOM based hand posture recognition technique is proposed for the description of simple hand postures. The experimental studies have confirmed DOMs as robust and efficient descriptors for user and view invariant representation of simple hand postures formed by simple finger configurations. This work aims at employing DOMs for the description of hand postures in Bharatanatyam and experimentally verify the robustness of DOMs in uniquely representing these complex hand postures.
The rest of the chapter is divided into four broad sections. The first section gives an brief introduction to Bharatanatyam, emphasizing the role of hand postures in representing the content of a Bharatanatyam dance.
The second section explains the posture acquisition and the database development procedures. The system implementation strategies and the experimental studies are presented in the third and the fourth sections respec- tively.