IoT Camera Item Search Using Machine Learning Without Tracking Tags
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Solution Overview
Problem
Conventional item tracking technologies, such as Bluetooth tracking devices and GPS-based solutions, are limited in their applicability, costly, and pose privacy and safety concerns, making it difficult to efficiently locate small, frequently moved items within a premises.
Innovation Solution
An item search service utilizing a set of IoT cameras that receive user input, access captured images, and employ a machine learning model to identify the item of interest, providing a search result through a user interface, with features like authentication, content filtering, and user-specific training to enhance accuracy and privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional Bluetooth tracking devices are used, then item tracking is possible, but cost increases and privacy concerns arise
Solution Approach 1:
The patent uses visual copying through camera images to track items instead of physical Bluetooth trackers. The machine learning model creates visual representations and comparisons of items from camera feeds, allowing tracking without attaching physical devices to items, thereby eliminating privacy concerns and costs associated with Bluetooth trackers.
Solution Approach 2:
The patent replaces the mechanical Bluetooth tracking system with an optical-computational system using cameras and machine learning. Instead of radio frequency communication and physical trackers, the system uses visual capture and AI-based identification to achieve item tracking, eliminating the need for additional hardware on items.
2Reliability
If GPS-based solutions are deployed, then location tracking is achieved, but applicability is limited and cost increases
Solution Approach 1:
The patent replaces GPS satellite-based positioning with an optical recognition system using cameras and machine learning. This substitution enables tracking to work indoors where GPS signals are unavailable, significantly improving adaptability to indoor premises while maintaining location tracking accuracy through visual identification.
Solution Approach 2:
The system uses existing IoT cameras for multiple purposes including security monitoring and item search, making the solution universally applicable across different settings without requiring specialized tracking hardware. The machine learning model can identify various types of items regardless of location, enhancing versatility.
3Ease of manufacture
If existing IoT cameras are utilized for item search, then cost is reduced, but item identification accuracy must be improved
Solution Approach 1:
The patent changes the parameters of existing cameras by using them in specific configurations optimized for item search, such as strategic placement and angle adjustment. The machine learning model processes images from these cameras with specialized algorithms that enhance identification accuracy, allowing standard cameras to achieve precision comparable to specialized tracking devices.
Solution Approach 2:
The system enables existing cameras to serve dual purposes: their original security function and new item search function. By leveraging the cameras' existing capabilities and adding software-based item identification through machine learning, the system achieves accurate item tracking without requiring additional hardware investment.
4Measurement precision
If machine learning models are trained with user data, then item recognition improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models with common item types before actual use. This allows the models to have baseline recognition capabilities immediately, while user-specific training can be performed incrementally in the background without significantly impacting real-time search performance.
Solution Approach 2:
The system implements feedback mechanisms where user corrections and confirmations during item searches are used to continuously refine and retrain the machine learning models. This feedback loop improves recognition accuracy over time while the training occurs in manageable increments that minimize disruption to system operation.
Data Source
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AI summary
Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for providing an item search service for a premises comprising a set of Internet of Things (IoT) cameras. An example embodiment operates by receiving, via a user interface of the item search service, first user input regarding an item of interest, wherein the first user input comprises one or more of speech input or text input, accessing a plurality of images of the premises captured by the set of IoT cameras, executing a machine learning model to identify one or more images in the plurality of images that include the item of interest based at least on the first user input, generating an item search result based on the identified one or more images, and providing the item search result via the user interface of the item search service.