Abstract:
State of the art techniques in the domain of video analysis have limitations in terms of capability to capture spatio-temporal representation. This limitation in turn affects interpretation of video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to capture spatio-temporal representation for video reconstruction and analysis. The method presents different architecture variations using three main deep network components: 2D convolution units, 3D convolution units and long short-term memory (LSTM) units for video reconstruction and analysis. These variations are trained for learning the spatio-temporal representation of the videos in order to generate a pre-trained video analysis module. By understanding the advantages and disadvantages of different architectural configurations, a novel architecture is designed for video reconstruction. Using transfer learning, the video reconstruction pre-trained model is extended to other video applications such as video object segmentation and surgical video tool segmentation.
Abstract:
The disclosure herein relates to methods and systems for generating an end-to-end de-smoking model for removing smoke present in a video. Conventional data-driven based de-smoking approaches are limited mainly due to lack of suitable training data. Further, the conventional data-driven based de-smoking approaches are not end-to-end for removing the smoke present in the video. The de-smoking model of the present disclosure is trained end-to-end with the use of synthesized smoky video frames that are obtained by source aware smoke synthesis approach. The end-to-end de-smoking model localize and remove the smoke present in the video, using dynamic properties of the smoke. Hence the end-to-end de-smoking model simultaneously identifies the regions affected with the smoke and performs the de-smoking with minimal artifacts. localized smoke removal and color restoration of a real-time video.
Abstract:
This disclosure relates generally to driver profiling, and more particularly to system and method for driver profiling corresponding to automobile trip. In one embodiment, the method includes selectively computing, based on GPS data captured during a trip, features such that a first set of features are computed at a computation device and a second set of features are computed at a cloud server. The first and second set of features differs in computational complexity level. Said features include statistical data for attributes computed from the GPS data. The features corresponding to the trip are stored at repository associated with the cloud server. The repository includes previously precomputed features data associated with a set of driver profiles for previously completed trips. A driver profile corresponding to the trip is identified from amongst the set of driver profiles based on comparison of the plurality of features and the previously computed features data.
Abstract:
The present disclosure provides wearable apparatus and method for calculating drift-free plantar pressure parameters for gait monitoring of an individual. Most conventional techniques use different kind of sensors placed in in-sole based wearable apparatus but are costly and not effective in calculating accurate plantar pressure parameters. The disclosed wearable apparatus uses off-the shelf piezoelectric sensors that are widely available in market with less cost. The drift-free plantar pressure parameters are calculated using drift-free static pressure data obtained by numerically integrating acquired dynamic sensor data from the piezoelectric sensors, using a LiTCEM correction mechanism. A 6-DOF Inertial Measurement Unit (IMU sensor) helps in isolating zero-pressure duration indicating when a foot of the individual is in air during a stride, while obtaining the drift-free static pressure data. The disclosed wearable apparatus calculate the drift-free plantar pressure parameters for long duration and facilitates monitoring walking patterns of the individual.
Abstract:
Embodiments of the present disclosure provide systems and methods that establish non-linear components of gyroscope errors which have not been studied or explored earlier. These errors include but are not limited to non-linear dynamic error which is a function of the angular velocity itself. Bias instability has been observed within the same environment of temperature and atmospheric pressure. In other words, the embodiments of the present disclosure analyse and models static bias errors and dynamic non-linear errors in the gyroscope sensor which may be used to model and correct errors accordingly In subsequent measurements. The system provide solution(s) when there is no way of directly estimating temperature for bias correction of gyroscope, by estimating a temperature change by considering indirect measurements from other sensors present onboard the device.
Abstract:
A method and system is provided for noise cleaning of photoplethysmogram signals. The method and system is disclosed for noise cleaning of photoplethysmogram signals for estimating blood pressure of a user; wherein photoplethysmogram signals are extracting from the user; the extracted photoplethysmogram signals are up sampled; the up sampled photoplethysmogram signals are filtered; uneven baseline drift of each cycle is removed from the up sampled and filtered photoplethysmogram signals; outlier cycles of the photoplethysmogram signals are removed and remaining cycles of the photoplethysmogram signals are modeled; and time domain features are extracted from originally extracted and modeled photoplethysmogram signals for estimating blood pressure of the user.
Abstract:
A system and a method for mobile sensing data processing are provided. The method includes, receiving one or more requests from one or more applications installed at a client device to obtain a processed sensing data obtained in response to execution of one or more tasks by the application using a set of sensors. Raw data is extracted from the set of sensors in response to the execution of the tasks. A data stream is configured to include sensor data and a task information associated with the tasks. The client device is connected with the server to transmit the data stream. The server outputs the processed sensing data upon processing the data stream and the task information by using one or more task specific models stored at the server. The processed sensing data is received from the server and provided to the applications.
Abstract:
Method(s) and System(s) for determining location of a user device within a premise are described. The method includes identifying multiple zones with physical boundaries within the premise based on parameters associated with geometry of the premise. The premise includes multiple access points distributed across the multiple zones. Thereafter, the method includes collecting a first set of Received Signal Strength Indicator (RSSI) Data that is representative of strength of signals received from each accessible access point, at different locations within the premise. After collecting the first set, the method includes computing a Variable Path Loss Exponent (VPLE) within each zone for each accessible access point for determining location of the user device based on at least one of the first set of RSSI data, a line of sight condition, a non-line of sight condition and distance between each accessible access point from each location.
Abstract:
A method and system is provided for noise cleaning of photoplethysmogram signals. The method and system is disclosed for noise cleaning of photoplethysmogram signals for estimating blood pressure of a user; wherein photoplethysmogram signals are extracting from the user; the extracted photoplethysmogram signals are up sampled; the up sampled photoplethysmogram signals are filtered; uneven baseline drift of each cycle is removed from the up sampled and filtered photoplethysmogram signals; outlier cycles of the photoplethysmogram signals are removed and remaining cycles of the photoplethysmogram signals are modeled; and time domain features are extracted from originally extracted and modeled photoplethysmogram signals for estimating blood pressure of the user.
Abstract:
A monitoring unit for vehicle monitoring comprising a receiving module configured to receive data from an OBD, wherein the data is associated with a plurality of jerks detected by a 3-axis accelerometer. The monitoring unit comprises an analytics module configured to compare an intensity of each jerk of the plurality of jerks to a predefined jerk threshold and capture high intensity jerks from the plurality of jerks. The high intensity jerks have intensity equal to or more than the predefined jerk threshold. The method further comprises determining an elapsed time for each of the high intensity jerks. The elapsed time for each of the high intensity jerks is compared to a predefined time threshold. Further it is determined whether an analysis on the high intensity jerks is to be performed at the vehicle or at a server located remotely.