vedez
commented
2 hours ago Note:
Research (References) Industry/Market/Current Best Practcies (Standards) Similar Solutions? New? Differences? FYP Solution Proposal?
Fr. Initial Project
Open vedez opened 2 hours ago
vedez
commented
2 hours ago Note:
Research (References) Industry/Market/Current Best Practcies (Standards) Similar Solutions? New? Differences? FYP Solution Proposal?
Fr. Initial Project
Similar Solutions: Clearview AI: Provides facial recognition technology widely used by law enforcement to detect individuals from large datasets of public photos.
Pros: High accuracy and huge database; widely adopted by law enforcement. Cons: Faces major legal and ethical challenges due to privacy concerns. Amazon Rekognition: An AWS-based AI service that can detect, analyze, and recognize faces in images and videos.
Pros: Easy integration with other AWS services; strong cloud-based solution. Cons: Privacy issues, not suitable for edge deployment in real-time applications. Google Cloud Vision AI: Offers pre-trained models to detect and recognize faces, human activities, and other objects.
Pros: Powerful API with strong integration into Google Cloud. Cons: Requires cloud connectivity; limited control over model customization. SenseTime: AI company specializing in facial recognition and human detection, offering solutions for smart cities, surveillance, and retail.
Pros: High performance in urban environments and large-scale deployments. Cons: Complex setup for global markets; high costs. OpenCV-based Solutions: Many open-source projects use OpenCV for building custom human detection and object tracking systems.
Pros: Completely customizable, widely used, and well-supported by the developer community. Cons: Requires significant coding and setup for tailored use cases. New Trends: 3D Human Detection: Advances in 3D cameras (e.g., LiDAR) enable more accurate detection of human poses and movements, crucial in robotics, autonomous vehicles, and interactive applications. AI-based Behavioral Analysis: Beyond basic detection, AI models are now focusing on analyzing human behaviors, postures, and even emotions in real time, enabling smarter decision-making systems. Lightweight AI Models for Edge Devices: There is increasing development of lightweight neural networks like MobileNet and TinyYOLO to make human detection feasible on low-power devices like Raspberry Pi or mobile phones. Ethical AI and Transparency: Given the rise of concerns about surveillance and personal privacy, companies are incorporating more transparency into their AI models and pushing for ethical usage guidelines. Integration with Augmented Reality (AR): Companies are exploring human detection in AR settings for enhanced user experiences, where the system can detect human gestures and actions for interactive content. Differences (FYP vs. Industry Solutions): Existing Solutions (Industry):
Highly specialized systems that target specific applications, such as security (Clearview AI), automotive (Tesla's Autopilot), or smart cities (SenseTime). Many are cloud-based (e.g., Amazon Rekognition, Google Cloud Vision) with limited real-time, on-premise capability. Ethical and privacy concerns are significant, especially for facial recognition-based systems. Your Proposed FYP Solution:
If your Final Year Project (FYP) focuses on building a human detection bot with tools like OpenCV, Raspberry Pi, and perhaps machine learning models, it can offer a cost-effective, lightweight, and edge-based solution compared to cloud-heavy, industry-grade products. Real-time edge processing: Unlike many cloud-dependent systems, your solution can process data locally (on Raspberry Pi), reducing latency and addressing privacy concerns by keeping sensitive data on-device. Customization: Your solution can be more customizable, allowing for specific behaviors, use cases, and simpler integration with hardware (cameras, sensors) on the Raspberry Pi.