Abstract: State of the art systems used for airport automation and data processing may be prone to data security related issues, as unauthorized personal may gain entry to sensitive data. The disclosure herein generally relates to airport management, and, more particularly, to a method and system for service authentication in an airport management network. The system uses a neural network to process a received service request and decides whether the service request is to be allowed or denied, based on a determined validity of the service request, role based access defined for a user requesting the service, a feature map data generated.

Abstract: This disclosure relates generally to method and system for live video streaming with integrated encoding and transmission semantics. The system receives a set of frames associated with a live video stream encoded to generate a set of data fragments using a reference encoder and a delta encoder. Transmitter unit of the live video streaming protocol transmits each packet of the set of full frames and the set of delta frames in sequence with a payload specific header based on a packet mode. Further, the receiver unit receives each packet of the full frames and each packet of the delta frames based on the packet mode to reconstruct an original sequence from the foreground pixels by estimating a total number of packets expected at each frame interval and loss incurred in each packet of the set of full frames and the set of delta frames.

Abstract: This relates generally to a method and a system for spatio-temporal polarization video analysis. The spatio-temporal polarization data is analyzed for a computer vision application such as object detection, image classification, image captioning, image reconstruction or image inpainting, face recognition and action recognition. Numerous classical and deep learning methods have been applied on polarimetric data for polarimetric imaging analysis, however, the available pre-trained models may not be directly suitable on polarization data, as polarimetric data is more complex. Further compared to analysis of the polarimetric images, a significant number of actions can be detected by polarimetric videos, hence analyzing polarimetric videos is more efficient. The disclosure is a spatio-temporal analysis of polarization video.

Abstract: The embodiments of present disclosure herein address problem of urban metabolism with respect to water demand and carbon dioxide emissions, the discussion is based on the reported data by the urban areas. The embodiments herein provide a method and system for estimating urban metabolism based on remotely sensed data. The system is configured to develop a model for identifying correct features from image or proxy features from image and then develop/use relation between the image feature or proxy feature from the image with the urban metabolic parameter. Further, the system develops an urban growth model which predicts spatial extent of the given proxy features. The urban growth scenario for each such conditions is different. By changing conditions of the model, different growth scenarios are played out. For each scenario, at least one urban metabolic parameter is predicted by taking output of the urban growth predictor.

Abstract: State of the art techniques in the domain of video analysis have limitations in terms of capability to extract the spatial and temporal information. This limitation in turn affects interpretation of the video data. The disclosure herein generally relates to video analysis, and, more particularly, to a method and system for video analysis to extract spatio-temporal information from a video being analyzed. The system uses a neural network architecture which has multiple layers to extract spatial and temporal information from the video being analyzed. The method of training the neural network that extracts a micro-scale information from a latent representation of the video is presented. This is generated using an attention network, which is then used to extract spatio-temporal information corresponding to the collected video, which is then used in multiple video analysis applications such as searching actions in videos, action detection and localization.

Abstract: This disclosure provides a method and system for spectrum matching for hyperspectral and multispectral data. Conventional methods using geometric or statistical distance measures for spectral matching considers two spectra having equal length or having large amplitude difference. These methods do not consider amplitude difference in the spectra or spectra with unequal lengths. Embodiments of the present disclosure is formulated as a measurement of transformation required for converting a target spectrum to a reference spectrum or vice versa. The method computes a transformation cost between the two spectra for spectral matching. The transformation cost is globally optimized to obtain an optimal transformation cost which represents the optimal spectrum matching of the target spectrum with the reference spectrum.

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.

Abstract: The embodiments herein provide a method and system that analyzes the pixel vectors by transforming the pixel vector into two-dimensional spectral shape space and then perform convolution over the image of graph thus formed. Method and system disclosed converts the pixel vector into image and provides a DCNN architecture that is built for processing 2D visual representation of the pixel vectors to learn spectral and classify the pixels. Thus, DCNN learn edges, arcs, arcs segments and the other shape features of the spectrum. Thus, the method disclosed enables converting a spectral signature to a shape, and then this shape is decomposed using hierarchical features learned at different convolution layers of the disclosed DCNN at different levels.

Abstract: Robots are deployed for handling different tasks in various field of applications. For the robots to function, task planning is required to be done. During the task planning, goal setting is done, as well as actions to be executed for corresponding to each goal are decided. Traditionally, this is carried out first and then the robots start executing the task plan, thereby failing to capture any change in the environment the robots operate, post the task plan generation. Disclosed herein is a method and system for robotic task planning in which a task plan is generated and is executed. However if the task execution fails due to change in any of the parameters/factors, then the system dynamically invokes an adaptation and re-planning mechanism which either updates the already generated task plan (by capturing the change) or generates a new task plan, which the robot can execute to achieve the goal.

Abstract: This disclosure relates generally to system and methods for dynamic scheduling and rescheduling using heterogeneous multi-agent fleet. The embodiments of present disclosure herein address unresolved problem of task allocation using a single solution which is not sufficient for handling various scenarios of multi-agent task allocation problems. For instance, the task allocation becomes more challenging in a scenario where each task has a deadline associated with it and execution time of the tasks cannot be pre-computed as they are dependent on previous tasks. The method of present disclosure provides a scalable solution for dynamic scheduling and rescheduling that handles tasks with multiple pickup and drop locations and dynamic execution time using the agents with heterogeneous speed in a more efficient manner, reducing indirect operating costs and increasing revenue potential while minimizing additional penalty due to run time delays that an agent may encounter.

Abstract: Conventional tele-presence robots have their own limitations with respect to task execution, information processing and management. Embodiments of the present disclosure provide a tele-presence robot (TPR) that communicates with a master device associated with a user via an edge device for task execution wherein control command from the master device is parsed for determining instructions set and task type for execution. Based on this determination, the TPR queries for information across storage devices until a response is obtained enough to execute task. The task upon execution is validated with the master device and user. Knowledge acquired, during querying, task execution and validation of the executed task, is dynamically partitioned by the TPR across storage devices namely, on-board memory of the tele-present robot, an edge device, a cloud and a web interface respectively depending upon the task type, operating environment of the tele-presence robot, and other performance affecting parameters.

Abstract: Traditional systems used for fashion attribute detection struggle to generate accurate predictions due to presence of large intra-class and relatively small inter-class variations in data related to the fashion attributes. The disclosure herein generally relates to image processing, and, more particularly, to a method and system for fashion attribute detection. The method proposes F-AttNet, an attribute extraction network to leverage the performance of fine-grained localized fashion attribute recognition. F-AttNet comprises Attentive Multi-scale Feature Encoder (AMF) blocks that encapsulate multi-scale fine-grained attribute information upon adaptive recalibration of channel weights. F-AttNet is designed by hierarchically stacking the AMF encoders to extract deep fine-grained information across multiple scales.

Abstract: Embodiments herein provide a method and system for continuously validating a user during an established authenticated session using Photoplethysmogram (PPG) and accelerometer data. State of the art approaches are mostly based on feature extraction and ML modelling for PPG based continuous session validation, while a template based approach in the art follows a complicated approach. The method disclosed herein utilizes less computation intensive template based approach to continuously validate the user across the session. The method comprises preprocessing a PPG data or PPG signal acquired from a wearable device worn by the user to identify segments of negligible motion. A first segment, after authentication using conventional authentication mechanism, serves as the initial reference. The chosen segments are then tested one by one with respect to the reference. If the templates in a segment match those of the reference, it is updated as the new reference, else a re-authentication is triggered.

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: Robotic platform for tele-presence applications has gained paramount importance, such as for remote meetings, group discussions, and the like and has sought much attention. There exist some robotic platforms for such tele-presence applications, these lack efficacy in communication and interaction between remote person and avatar robot deployed in another geographic location thus adding network overhead. Embodiments of the present disclosure for edge centric communication protocol for remotely maneuvering tele-presence robot in geographically distributed environment.

Abstract: Automation is the key to build efficient workflows with minimum effort consumption. However, there is a large gap in workflow synthesis for automated AI application development. Computer vision workflow synthesis largely rely on domain expert due to lack of generalization over solution search space for given goal. This search space for creating suitable solution(s) using available algorithms is quite vast, which makes exploratory work of solution building a time-, effort- and intellect intensive endeavor. Embodiments of the present disclosure provide system and method for goal-driven algorithm selection approach for building computer vision workflows on the fly. The system generates one or more task workflows with associated success probability depending on initial conditions and input natural language goal query by combining various image processing algorithms. Symbolic AI planning is aided by Reinforcement Learning to recommend optimal workflows that are robust and adaptive to changes in the environment.

Abstract: Embodiments of the present disclosure provide a method and system for co-operative and cascaded inference on the edge device using an integrated Deep Learning (DL) model for object detection and localization, which comprises a strong classifier trained on largely available datasets and a weak localizer trained on scarcely available datasets, and work in coordination to first detect object (fire) in every input frame using the classifier, and then trigger a localizer only for the frames that are classified as fire frames. The classifier and the localizer of the integrated DL model are jointly trained using Multitask Learning approach. Works in literature hardly address the technical challenge of embedding such integrated DL model to be deployed on edge devices. The method provides an optimal hardware software partitioning approach for components or segments of the integrated DL model which achieves a tradeoff between latency and accuracy in object classification and localization.

Abstract: The disclosure herein relates to methods and systems for localized smoke removal and color restoration of a real-time video. Conventional techniques apply the de-smoking process only on a single image, by finding the regions having the smoke, based on manual air-light estimation. In addition, regaining original colors of de-smoked image is quite challenging. The present disclosure herein solves the technical problems. In the first stage, video frames having the smoky and smoke-free video frames are identified, from the video received in the real-time. In the second stage, an air-light is estimated automatically using a combined feature map. An intermediate de-smoked video frame for each smoky video frame is generated based on the air-light using a de-smoking algorithm. In the third and the last stage, a smoke-free video reference frame is used to compensate for color distortions introduced by the de-smoking algorithm in the second stage.

Abstract: A growing need for inferencing to be run on fog devices exists, in order to reduce the upstream network traffic. However, being computationally constrained in nature, executing complex deep inferencing models on such devices has been proved difficult. A system and method for partitioning of deep convolution neural network for execution of computationally constraint devices at a network edge has been provided. The system is configured to use depth wise input partitioning of convolutional operations in deep convolutional neural network (DCNN). The convolution operation is performed based on an input filter depth and number of filters for determining the appropriate parameters for partitioning based on an inference speedup method. The system uses a master-slave network for partitioning the input. The system is configured to address these problems by depth wise partitioning of input which ensures speedup inference of convolution operations by reducing pixel overlaps.

Abstract: State of art techniques performing image labeling of remotely sensed data are computation intensive, consume time and resources. A method and system for efficient retrieval of a target in an image in a collection of remotely sensed data is disclosed. Image scanning is performed efficiently, wherein only a small percentage of pixels from the entire image are scanned to identify the target. One or more samples are intelligently identified based on sample selection criteria and are scanned for detecting presence of the target based on cumulative evidence score Plurality of sampling approaches comprising active sampling, distributed sampling and hybrid sampling are disclosed that either detect and localize the target or perform image labeling indicating only presence of the target.