Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. Our approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-of the-art HR estimation methods in naturalistic conditions.

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Self-Adaptive Matrix Completion for Heart Rate Estimation from Face Videos under Realistic Conditions

In various embodiments, methods, systems, and computer program products for processing digital images captured by a mobile device are disclosed. Myriad features enable and/or facilitate processing of such digital images using a mobile device that would otherwise be technically impossible or impractical, and furthermore address unique challenges presented by images captured using a camera rather than a traditional flat-bed scanner, paper-feed scanner or multifunction peripheral.

Systems and methods for mobile image capture and processing

A method is disclosed for mitigating the risks associated with driving by assigning risk values to road segments and using those risk values to select less risky travel routes. Various approaches to helping users mitigate risk are presented. A computing device is configured to generate a database of risk values. That device may receive accident information, geographic information, vehicle information, and other information from one or more data sources and calculate a risk value for the associated road segment. Subsequently, the computing device may provide the associated risk value to other devices. Furthermore, a personal navigation device may receive travel route information and use that information to retrieve risk values for the road segments in the travel route. An insurance company may use this information to determine whether to adjust a quote or premium of an insurance policy. This and other aspects relating to using geographically encoded information to promote and reward risk mitigation are disclosed.

Route Risk Mitigation

An adaptive audio watermarking based on the singular value decomposition in the wavelet domain

The Kinect sensing devices have been widely used in current Human-Computer Interaction entertainment. A fundamental issue involved is to detect users’ motions accurately and quickly. In this paper, we tackle it by proposing a linear algorithm, which is augmented by feature interaction. The linear property guarantees its speed whereas feature interaction captures the higher order effect from the data to enhance its accuracy. The Schatten-p norm is leveraged to integrate the main linear effect and the higher order nonlinear effect by mining the correlation between them. The resulted classification model is a desirable combination of speed and accuracy. We propose a novel solution to solve our objective function. Experiments are performed on three public Kinect-based entertainment data sets related to fitness and gaming. The results show that our method has its advantage for motion detection in a real-time Kinect entertaining environment.

Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection

Last few years have witnessed rapid growth in video coding technology. Among various standards, H. 264/Advanced Video Codec (AVC) is found to be of significant importance regarding reduced bandwidth, better image quality and network friendliness. One of the current fields of interest is to develop a system with authentication and copyright protection methodology embedded within an efficient video codec. In this paper we first perform a survey on available video watermarking techniques, feasibility study on watermarking techniques meeting application specific criteria for H.264/AVC and then we perform a comparative analysis based on robustness and computational complexity of different watermarking algorithms.

A Survey on Different Video Watermarking Techniques and Comparative Analysis with Reference to H.264/AVC

In recent times, most of the industries provide transportation facility for their employees from scheduled pick-up and drop points. In order to reduce longer waiting time, it is important to accurately predict the vehicle arrival in real time. This paper proposes a simple, lightweight yet powerful historical data based vehicle arrival time prediction model. Unlike previous work, the proposed model uses very limited input features namely vehicle trajectory and timestamp considering the scarcity and unavailability of data in the developing countries regarding traffic congestion, weather, scheduled arrival time, leg time, dwell time etc. The authors proposed model is evaluated against standard Artificial Neural Network (ANN) and Support Vector Machine (SVM) regression models using real bus data of an industry campus at Siruseri, Chennai collected over four months of time period. The result shows that proposed historical data based model can predict two and half (approx.) times faster than ANN model and two (approx.) times faster than SVM model while it also achieves a comparable accuracy (75.56%) with respect to ANN model (76%) and SVM model (71.3%). Hence, the proposed historical data based model is capable of providing a real time system by balancing the trade-off between prediction time and prediction accuracy.

Historical Data Based Real Time Prediction of Vehicle Arrival Time

A method for monitoring physiological parameters associated with a subject using a hand held device is described herein. In an implementation, the method includes obtaining a plurality of sample photoplethysmographic (PPG) features associated with a sample subject, from a video of a body part of the sample subject. From among the plurality of sample PPG features, at least one relevant sample PPG feature associated with the physiological parameter, is selected based on a ground truth value of the physiological parameter for the subject. Further, based on the at least one relevant sample PPG feature and the ground truth value of the physiological parameter, a mathematical model indicative of a correlation between the relevant sample PPG feature and the physiological parameter, is determined. The mathematical model can be deployed for monitoring the physiological parameter in real time.

Monitoring physiological parameters

An application development system for development of Internet of Things (IoT) application includes a cataloging module to obtain an input from an application developer. The input comprises data related to the IoT application to be developed. The cataloging module further retrieves a plurality of reusable artefacts from a knowledge database based on the input. A recommendation module in the application development system recommends, to the application developer, artefacts from amongst the plurality of reusable artefacts, based at least on one of a feedback associated with each of the plurality of reusable artefacts, an expert analysis, and a combination of the expert analysis and the feedback. An association module in the application development system associates artefacts selected by the application developer with each other for development of the IoT application.

Internet of things (iot) application development

A system and method for a real-time prognosis of a vehicle comprising a personal communication device comprising an arbitrarily oriented three-axis accelerometer configured to capture a pitch motion and/or roll motion of the vehicle and an onboard diagnostics system communicably connected with the personal communication device enabling bi-directional communication. The personal communication device comprising a processor configured for geometric mapping of a three dimensional Cartesian coordinate of the three-axis accelerometer with the vehicle. The processor virtually orients the coordinates of three-axis accelerometer to coincide with the coordinates of the vehicle. The arbitrarily oriented three-axis accelerometer is configured to capture a road condition and a driver behaviour using a sampling rate between 4 Hertz (Hz) to 10 Hertz (Hz). The system for the real-time prognosis of the vehicle, wherein the real-time prognosis utilizes at least one predictive analysis model to determine real-time prognosis for the said vehicle.

Tanushyam Chattopadhyay

Papers

A Survey on Different Video Watermarking Techniques and Comparative Analysis with Reference to H.264/AVC

Historical Data Based Real Time Prediction of Vehicle Arrival Time

Monitoring physiological parameters

Internet of things (iot) application development

A system and a method for improved car prognosis