Abstract: System and method for evaluating a cognitive load on a user, corresponding to a stimulus is disclosed. Electroencephalogram (EEG) data corresponding to the stimulus of a user is received. The stimulus corresponds to a mental task performed by the user. The EEG data is split into a plurality of slots. A slot of the plurality of slots comprises a subset of the EEG data. One or more EEG features are extracted from the subset of the EEG data. The one or more EEG features are represented in one of a frequency domain and a time domain. A plurality of data points present in the one or more EEG features is grouped into two or more clusters using an unsupervised learning technique. The two or more clusters comprise one or more data points of the plurality of data points. The one or more data points correspond to a level of the cognitive load.

Abstract: Disclosed is a method and system for selection of Electroencephalography (EEG) channels valid for determining cognitive load of subject. According to one embodiment, EEG signals are obtained from EEG channels associated with subject performing cognitive tasks are received. Time-frequency features of EEG signals are extracted for a frequency band comprise maximum energy value, minimum energy value, average energy value, maximum frequency value, minimum frequency value, and average frequency value. Weight of an EEG channel associated with time-frequency feature is derived using statistical learning technique. Binary values for EEG channels corresponding to time-frequency feature are assigned using weight of EEG channel associated with time-frequency feature.

Abstract: System and method for evaluating a cognitive load on a user, corresponding to a stimulus is disclosed. Electroencephalogram (EEG) data corresponding to the stimulus of a user is received. The stimulus corresponds to a mental task performed by the user. The EEG data is split into a plurality of slots. A slot of the plurality of slots comprises a subset of the EEG data. One or more EEG features are extracted from the subset of the EEG data. The one or more EEG features are represented in one of a frequency domain and a time domain. A plurality of data points present in the one or more EEG features is grouped into two or more clusters using an unsupervised learning technique. The two or more clusters comprise one or more data points of the plurality of data points. The one or more data points correspond to a level of the cognitive load.

Abstract: A method and system for tracking of objects in a video is disclosed. The method of the present invention enables user to indicate a boundary-box to the identified object of interest in the broadcast video on television or any other communication media. The object indicted in the boundary-box is than tracked by the users connected in a social community network in the upcoming video frames of the broadcasted video. The tracked object is then tagged by the users in the social community network. Further, the present invention enables augmentation of the tracked object in the video by extracting additional information from the online service providers. The augmentation and tagging of the object generates metadata related to the object. The metadata generated is stored on a server to track the object in future based on the metadata related to the object.

Abstract: Disclosed is a method and system for selection of Electroencephalography (EEG) channels valid for determining cognitive load of subject. According to one embodiment, EEG signals are obtained from EEG channels associated with subject performing cognitive tasks are received. Time-frequency features of EEG signals are extracted for a frequency band comprise maximum energy value, minimum energy value, average energy value, maximum frequency value, minimum frequency value, and average frequency value. Weight of an EEG channel associated with time-frequency feature is derived using statistical learning technique. Binary values for EEG channels corresponding to time-frequency feature are assigned using weight of EEG channel associated with time-frequency feature.

Abstract: Method(s) and system(s) for identification of an unknown person are disclosed. The method includes receiving skeleton data comprises data of multiple skeleton joints of the unknown person from skeleton recording devices. The method further includes extracting G gait feature vectors from the skeleton data. Further, the method includes classifying each gait feature vector into one of N classes based on a training dataset for N known persons and computing a classification score for each class. The method also includes clustering the training dataset into M clusters based on M predefined characteristic attributes of the known persons, tagging each gait feature vector with one of the M clusters based on a distance between a respective gait feature vector and cluster centers of M clusters, and determining a clustering score for each M cluster. The method further includes identifying the unknown person based on clustering scores and classification scores.

Abstract: The present invention provides a method and system for implementation of an interactive television application. Particularly, the invention provides method and system for designing and implementation of synchronized interactive television applications using quick response (QR) code, wherein the quick response (QR) code is used to tag the broadcasted television content. More particularly, the invention provides method and system for decoding the quick response (QR) code at the receiver side to provide a universal resource locator (URL) which hosts augmented services for the content on the interactive television as well as on the secondary communication device.

Abstract: Method(s) and system(s) for identification of an unknown person are disclosed. The method includes receiving skeleton data comprises data of multiple skeleton joints of the unknown person from skeleton recording devices. The method further includes extracting G gait feature vectors from the skeleton data. Further, the method includes classifying each gait feature vector into one of N classes based on a training dataset for N known persons and computing a classification score for each class. The method also includes clustering the training dataset into M clusters based on M predefined characteristic attributes of the known persons, tagging each gait feature vector with one of the M clusters based on a distance between a respective gait feature vector and cluster centers of M clusters, and determining a clustering score for each M cluster. The method further includes identifying the unknown person based on clustering scores and classification scores.

Abstract: A method and system for tracking of objects in a video is disclosed. The method of the present invention enables user to indicate a boundary-box to the identified object of interest in the broadcast video on television or any other communication media. The object indicted in the boundary-box is than tracked by the users connected in a social community network in the upcoming video frames of the broadcasted video. The tracked object is then tagged by the users in the social community network. Further, the present invention enables augmentation of the tracked object in the video by extracting additional information from the online service providers. The augmentation and tagging of the object generates metadata related to the object. The metadata generated is stored on a server to track the object in future based on the metadata related to the object.

Abstract: The present invention provides a method and system for implementation of an interactive television application. Particularly, the invention provides method and system for designing and implementation of synchronized interactive television applications using quick response (QR) code, wherein the quick response (QR) code is used to tag the broadcasted television content. More particularly, the invention provides method and system for decoding the quick response (QR) code at the receiver side to provide a universal resource locator (URL) which hosts augmented services for the content on the interactive television as well as on the secondary communication device.