DEEP LEARNING-ENHANCED INTERFACE FOR WEBCAM DEVICE

Abstract

The present invention discloses a Deep Learning-Enhanced Interface for Webcam Device that integrates advanced hardware and software components to improve video capture quality, real-time analysis, and interactive functionalities. The system includes a high-definition image sensor, Digital Signal Processor (DSP), Field Programmable Gate Array (FPGA), and a custom Neural Processing Unit (NPU) to accelerate deep learning tasks. It employs pre-trained neural networks for object recognition, facial analysis, and gesture control. A Hybrid Computing Architecture enables edge and cloud processing, while a Real-Time Operating System (RTOS) ensures low-latency performance. Additional features include Dynamic Video Optimization, Real-Time Background Segmentation, and a Gesture Recognition Engine for hands-free interaction. The system supports high-fidelity audio capture and secure data transmission, making it ideal for applications in teleconferencing, online education, and surveillance. The invention addresses limitations of traditional webcams, offering enhanced video quality, adaptive learning, and improved user privacy.

Specification

Description:[015] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments. On the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit, and scope of the present disclosure as defined by the appended claims.
[016] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[017] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail to avoid obscuring the embodiments.
[018] Also, it is noted that individual embodiments may be described as a process that is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
[019] The word "exemplary" and/or "demonstrative" is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as "exemplary" and/or "demonstrative" is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms "includes," "has," "contains," and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term "comprising" as an open transition word without precluding any additional or other elements.
[020] Reference throughout this specification to "one embodiment" or "an embodiment" or "an instance" or "one instance" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[021] In an embodiment of the invention and referring to Figures 1, the present invention relates to a Deep Learning-Enhanced Interface for Webcam Device, designed to leverage advanced deep learning algorithms to improve video capture quality, real-time analysis, and interactive functionalities. The system integrates both hardware and software components to provide a sophisticated solution that addresses the limitations of conventional webcam technology. The invention encompasses a variety of novel features, including real-time image enhancement, object recognition, facial analysis, and gesture-based control, making it applicable to numerous fields such as teleconferencing, online education, and surveillance.
[022] The hardware component of the invention comprises a high-definition (HD) webcam equipped with a specialized image sensor array, optimized for capturing high-resolution images and videos in various lighting conditions. The image sensor is paired with a dedicated Digital Signal Processor (DSP) and a Field Programmable Gate Array (FPGA) to enable efficient real-time processing of video streams. The DSP is tasked with handling tasks such as noise reduction, edge enhancement, and dynamic range compression, while the FPGA accelerates the execution of deep learning algorithms, ensuring low-latency performance.
[023] The core innovation lies in the integration of deep learning algorithms directly within the webcam device. The software component includes a suite of pre-trained convolutional neural networks (CNNs) and recurrent neural networks (RNNs) optimized for tasks like facial recognition, object detection, and gesture recognition. The CNNs are responsible for extracting spatial features from the video frames, while the RNNs analyze temporal patterns to enable advanced functionalities like motion tracking and activity recognition. These neural networks are deployed on the webcam's internal processing unit, reducing the reliance on external computing resources.
[024] To optimize the performance of the deep learning models, the system uses a Hybrid Computing Architecture. This architecture combines edge computing and cloud-based processing, allowing the device to offload complex tasks to a remote server when required. The edge component handles real-time tasks such as video capture and initial preprocessing, while the cloud component is utilized for more computationally intensive operations like deep learning model updates and retraining. The communication between the edge and cloud components is managed through a secure, low-latency network protocol.
[025] One of the key hardware innovations in this invention is the inclusion of a Custom Neural Processing Unit (NPU) embedded within the webcam. The NPU is specifically designed to accelerate deep learning computations, enabling real-time inference on high-resolution video streams. The NPU supports a variety of neural network architectures, including CNNs, RNNs, and transformers, making it versatile for different AI applications. The NPU also includes a built-in Tensor Acceleration Module (TAM) that optimizes the performance of matrix operations, which are fundamental to deep learning computations.
[026] The software framework leverages a Real-Time Operating System (RTOS) to manage the scheduling of tasks across the various hardware components. The RTOS ensures that time-critical tasks, such as video frame analysis and deep learning inference, are prioritized, resulting in minimal latency. The system also includes an adaptive learning mechanism that enables the deep learning models to update themselves based on new data, ensuring that the webcam continues to improve its performance over time. This self-learning capability is particularly beneficial for applications such as surveillance, where the system can adapt to changing environments and detect anomalies.
[027] The integration of hardware and software components is facilitated by a Unified Interface Controller (UIC), which acts as a communication bridge between the various modules of the system. The UIC coordinates the flow of data between the image sensor, DSP, FPGA, and NPU, ensuring that each component operates in sync. This coordination is crucial for maintaining the high performance of the device, as it prevents bottlenecks and ensures efficient utilization of resources.
[028] To validate the effectiveness of the invention, a series of experiments were conducted comparing the performance of the Deep Learning-Enhanced Webcam Device against traditional webcams. The tests evaluated various parameters, including frame rate, latency, accuracy of object detection, and power consumption. Table 1 shows a summary of the performance comparison.

[029] As shown in Table 1, the deep learning-enhanced webcam device outperforms traditional webcams in terms of frame rate, latency, and object detection accuracy, with only a marginal increase in power consumption. This demonstrates the efficiency of the hardware-accelerated deep learning algorithms implemented in the device.
[030] The system also incorporates a Dynamic Video Optimization Module (DVOM) that automatically adjusts video parameters based on the analysis of real-time conditions. This module uses a feedback loop to monitor video quality metrics such as brightness, contrast, and sharpness, and adjusts the settings to enhance image clarity. The DVOM is particularly useful in low-light conditions, where traditional webcams struggle to maintain image quality.
[031] The invention further includes a Gesture Recognition Engine (GRE), which uses deep learning to detect and interpret user gestures. The GRE is capable of recognizing a wide range of gestures, such as hand waves, thumbs up, and pointing, enabling a hands-free interaction with the device. The gesture recognition functionality is particularly useful in applications such as virtual meetings and online presentations, where users can control the camera with simple gestures.
[032] To support the advanced functionalities, the webcam is equipped with a Multi-Directional Microphone Array and a Beamforming Audio Processor. These components enable the system to capture high-fidelity audio and reduce background noise, enhancing the overall user experience during video calls. The audio data is also analyzed by a deep learning model to enable features like voice-activated commands and speaker recognition.
[033] Another novel feature of this invention is the Real-Time Background Segmentation Module (RBSM), which uses deep learning to separate the foreground from the background in live video streams. This module allows users to replace their background with custom images or blur it for privacy during video calls. The RBSM leverages a lightweight segmentation model optimized for real-time performance, ensuring smooth and seamless background replacement.
[034] The deep learning models used in this system are trained on a vast dataset comprising millions of annotated images and videos, ensuring high accuracy across diverse scenarios. The training process involves techniques such as data augmentation, transfer learning, and fine-tuning to enhance the model's robustness. Additionally, the models are periodically updated with new data collected from user interactions, ensuring that the system remains up-to-date with the latest advancements in AI.
[035] The system also features an Intelligent Power Management System (IPMS) that dynamically adjusts the power usage of the device based on the workload. The IPMS monitors the activity levels of the various components and scales the power consumption accordingly, extending the device's operational life. This feature is particularly beneficial for portable applications, where battery life is a critical factor.
[036] To further enhance user privacy, the invention includes a Secure Data Encryption Module (SDEM) that encrypts all video and audio data transmitted over the network. The SDEM uses advanced encryption standards (AES) to protect user data from unauthorized access, making the system suitable for use in sensitive applications such as telemedicine and remote work.
[037] In conclusion, the present invention provides a comprehensive solution for enhancing the functionality and performance of webcam devices using deep learning. By integrating novel hardware components such as a custom NPU and software features like gesture recognition and background segmentation, this invention addresses the limitations of existing webcam technology. The invention is poised to set a new standard for webcam devices, offering a powerful tool for a wide range of applications where high-quality video and interactive capabilities are essential. , Claims:1. A Deep Learning-Enhanced Interface for Webcam Device comprising:
a) a high-definition image sensor array configured to capture high-resolution images and videos;
b) a Digital Signal Processor (DSP) for real-time noise reduction, edge enhancement, and dynamic range compression;
c) a Field Programmable Gate Array (FPGA) to accelerate deep learning algorithm execution;
d) a Custom Neural Processing Unit (NPU) with a Tensor Acceleration Module (TAM) for real-time deep learning inference;
e) a suite of pre-trained deep learning models including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for object recognition, facial analysis, and gesture control;
f) a Real-Time Operating System (RTOS) to manage task scheduling and ensure low-latency performance;
wherein the system is configured to enhance video capture quality, enable real-time analysis, and support interactive functionalities.
2. The device as claimed in Claim 1, wherein the deep learning models are trained using a Hybrid Computing Architecture that combines edge computing for real-time tasks and cloud-based processing for deep learning model updates and retraining.
3. The device as claimed in Claim 1, further includes a Unified Interface Controller (UIC) that coordinates data flow between the image sensor, DSP, FPGA, and NPU, ensuring synchronized operation of all hardware components.
4. The device as claimed in Claim 1, wherein the Dynamic Video Optimization Module (DVOM) automatically adjusts video parameters such as brightness, contrast, and sharpness based on real-time analysis of video quality metrics.
5. The device as claimed in Claim 1, further includes a Gesture Recognition Engine (GRE) that detects and interprets user gestures, enabling hands-free control of the webcam device for applications like virtual meetings and online presentations.
6. The device as claimed in Claim 1, wherein the system includes a Real-Time Background Segmentation Module (RBSM) using deep learning to separate foreground and background, allowing for background replacement or blurring during live video streams.
7. The device as claimed in Claim 1, further includes a Multi-Directional Microphone Array and a Beamforming Audio Processor to capture high-fidelity audio, reduce background noise, and enable voice-activated commands and speaker recognition.
8. The device as claimed in Claim 1, wherein the software framework includes an Adaptive Learning Mechanism that allows the deep learning models to update based on new data, ensuring continuous improvement of video analysis and interactive features.
9. The device as claimed in Claim 1, further includes an Intelligent Power Management System (IPMS) that dynamically adjusts power consumption based on workload, optimizing the device's operational life for portable applications.
10. The device as claimed in Claim 1, wherein all video and audio data transmitted over the network is secured using a Secure Data Encryption Module (SDEM) that employs Advanced Encryption Standards (AES) to protect user privacy during teleconferencing and remote work.

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