IoT Camera Item Search Using Machine Learning Instead of Trackers
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Solution Overview
Problem
Existing 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 and locate items of interest, with features for user authentication, content filtering, and privacy protection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Bluetooth tracking devices or GPS-based solutions are used, then item location capability is provided, but cost increases and privacy/safety concerns arise
Solution Approach 1:
The patent creates a visual copy of the item through camera images and uses machine learning to recognize and locate the item in these images, replacing the need for physical tracking devices on the item itself. This eliminates the privacy and safety issues associated with electronic tracking devices while maintaining item location capability.
Solution Approach 2:
The patent replaces mechanical/electronic tracking mechanisms (Bluetooth devices, GPS trackers) with an optical system using cameras and machine learning vision algorithms. This substitution eliminates the need for electronic tracking hardware on items, thereby resolving privacy and safety concerns while maintaining location functionality.
2Ease of operation
If existing item tracking technologies are deployed, then item search functionality is achieved, but device complexity and cost increase
Solution Approach 1:
The patent makes existing IoT cameras perform multiple functions: they continue to provide security monitoring while simultaneously enabling item search and location capabilities through machine learning integration. This multi-functionality approach avoids adding separate dedicated tracking hardware, reducing overall system complexity.
Solution Approach 2:
The system uses the cameras' existing capability to capture images and processes these images through machine learning models that automatically identify and locate items without requiring additional active tracking components. The system serves itself by leveraging already-deployed hardware resources rather than adding complex new infrastructure.
3Measurement precision
If machine learning model is trained on user-specific images and labels, then item identification accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary training of the machine learning model during the setup phase using a small set of user-provided images and labels. Once trained, the model can quickly identify items in new images without requiring retraining, thus achieving high accuracy while minimizing ongoing computational time and resource consumption.
Solution Approach 2:
The patent changes the approach from continuous training to one-time or periodic training with fine-tuning. The machine learning model is initially trained on a small dataset of user-specific images and labels, then deployed for inference. This parameter change in the training process significantly reduces computational time requirements while maintaining high identification accuracy.
Data Source
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.