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Speech impaired people use hand based gestures to communicate. Unfortunately, the vast majority of the people are not aware of the semantics of these gestures. In a attempt to bridge the same, we propose a real time hand gesture recognition system based on the data captured by the Microsoft Kinect RGB-D camera. Given that there is no one to one mapping between the pixels of the depth and the RGB camera, we used computer vision techniques like 3D contruction and affine transformation. After achieving one to one mapping, segmentation of the hand gestures was done from the background noise. Convolutional Neural Networks (CNNs) were utilised for training 36 static gestures relating to Indian Sign Language (ISL) alphabets and numbers. The model achieved an accuracy of 98.81% on training using 45,000 RGB images and 45,000 depth images. Further Convolutional LSTMs were used for training 10 ISL dynamic word gestures and an accuracy of 99.08% was obtained by training 1080 videos. The model showed accurate real time performance on prediction of ISL static gestures, leaving a scope for further research on sentence formation through gestures. The model also showed competitive adaptability to American Sign Language (ASL) gestures when the ISL models weights were transfer learned to ASL and it resulted in giving 97.71% accuracy.

Visual Speech Recognition (VSR) deals with the task of extracting speech information from visual cues from a person's face while speaking. Accurate lip segmentation and modeling are essential in any VSR algorithm for good feature extraction. However, lip modeling is a complicated task and is not very robust in natural conditions. This paper describes a novel technique for limited vocabulary visual-only speech recognition that does not use lip modeling. For visual feature extraction, Discrete Cosine Transform (DCT) and Local Binary Pattern (LBP) have been tested. An Error-Correcting Output Codes (ECOC) multi-class model using Support Vector Machine (SVM) binary learners is used for recognition and classification of words.

The proposed Brain-Computer Interface system enables Quadriplegic patients, people with severe motor disabilities to send commands to electronic devices and communicate with ease. Interactive sessions are vital for effective knowledge transfer in any learning eco-system. The growth of Brain-Computer Interface (BCI) has led to rapid development in 'Assistive Systems' for the disabled called 'assistive domotics'. Brain-Computer-Interface is capable of reading the brainwaves of an individual and analyse it to obtain some meaningful data. This processed data can be used to assist people having speech disorders and sometimes people with limited locomotion to communicate. In this Project, Emotiv EPOC Headset is used to obtain the electroencephalogram (EEG). The obtained data is processed to communicate pre-defined commands and queries for interactive learning. EEG data can also be used to monitor student's emotional behaviour and provide emotional feedback to the students. Other Vital Information like the heartbeat, blood pressure, ECG and temperature are monitored and uploaded to the server. The Data is processed in Intel Edison, system on chip (SoC). Patient metrics are displayed via Intel IoT Analytics cloud service.

Enhanced Local Ternary Patterns (ELTP) significantly improves performance over other feature descriptor methods including Local Binary Patterns (LBP) and Local Ternary Patterns (LTP). Sequential implementation of ELTP results in poor performance in terms of execution time for real time systems. Speed and accuracy are important characteristics of a real time face recognition system. With the aim of fulfilling both these criteria, this paper presents an implementation of GPU Accelerated Face Recognition System with ELTP using OpenCL framework. As a result of our Optimization techniques, we have achieved highest kernel execution speedup of 374x for ELTP image and histogram generation with 4096 x H 4096 (16MP) image resolution when it is implemented on GPU. Face recognition with ELTP showed higher recognition rates on ORL database. We also implemented LBP and LTP algorithms on GPU and compared their performances with ELTP. Similar Optimization techniques were applied for LBP kernel executions, which resulted in much higher speedups when compared to their previous implementations. Experimental results demonstrated that Parallel implementation with ELTP on GPU (AMD Radeon HD 7650M) outperforms CPU based face recognition system using LBP in terms of speed and accuracy.

Distributed processing is a new paradigm to analyse the huge volume of video data in camera networks. This paper addresses the problem of distributed single target tracking considering false positives and missed detections. Target tracking is modelled as a dynamic state estimation problem with nonlinear process and measurement model. We propose to use the sigma point information filters combined with a consensus algorithm. Sigma point information filters are integrated with probabilistic data association filter to deal with false positives and missed detections. We use a distributed average consensus algorithm which converges in finite time. Unlike other related state of the art technique papers, we report results on real data and show the effectiveness of the proposed algorithm.

Computers have become an essential part of our lives. Many advances have occurred in these machines over past decade in terms of new faster processors, energy efficiency, HD monitors etc. but the basic input devices such as mouse and keyboard remained the same. This project aims at giving a better mode of human computer interaction by allowing computers to respond to human hand gestures directly. The device aims at using the same interface that a mouse uses for interaction with a computer and thus it can be used in any computer without much modification to its software environment.

In this paper, we implement a novel parallelized approach of Local Binary Pattern (LBP) based face recognition algorithm on GPU. High performance rates have been achieved through maximizing the resource exploitation available in the GPU. The launch of GPU programming tools like Open source Computation Language (OpenCL) and (CUDA) have boosted the development of various applications on GPU. In this paper we implement a parallelized LBP algorithm on GPU using OpenCL programming tools. Programs developed under the OpenCL enable us to utilize GPU for general purpose computation with increased performance efficiency in terms of execution time. The experimental results based on the implementation on AMD 6500 GPU processor are observed to increase the computational performance of the system by to 30 folds in case of 1024×1024 images. The relative computational efficiency increases with increase in the size of the Image. This paper addresses several parallelization problems related to memory access and updating, divergent execution paths, understanding and realizing the OpenCL's concurrency and Execution models.

With the advent of Internet, video over IP is gaining popularity. In such an enviroment, scalability and fault tolerance will be the key issues. Existing video on demand (VoD) service systems [1], [2], [3] are usually neither scalable nor tolerant to server faults and hence fail to comply to multi-user, failure-prone networks such as the Internet. Current research areas concerning VoD often focus on increasing the throughput and reliability of single server, but rarely addresses the smooth provision of service during server as well as network failures. Reliable Server Pooling (RSerPool), being capable of providing high availability by using multiple redundant servers as single source point, can be a solution to overcome the above failures. During a possible server failure, the continuity of service is retained by another server. In order to achieve transparent failover, efficient state sharing is an important requirement. In this paper, we present an elegant, simple, efficient and scalable approach which has been developed to facilitate the transfer of state by the client itself, using extended cookie mechanism, which ensures that there is no noticeable change in disruption or the video quality.