Real-Time Scene Detection Using Decision Tree Traversal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing scene detection methods in wireless communication apparatuses, such as smartphones and tablets, face challenges in accurately and reliably classifying scenes in real-time due to the reliance on multiple sensors and algorithms, which can be power-intensive and inefficient in battery-powered devices.
Innovation Solution
The method involves selecting the most appropriate attributes for scene discrimination using a decision tree algorithm, accompanied by a confidence index calculation to enhance detection reliability, and includes a preliminary phase to determine the relevance of attributes for scene classification, utilizing sensors like accelerometers, gyroscopes, and audio sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and algorithms are used for scene detection, then detection accuracy is improved, but power consumption increases
Solution Approach 1:
The patent segments the scene detection process into distinct phases: a training phase where multiple sensors and algorithms are used to build decision trees, and an execution phase where pre-computed decision trees are traversed using minimal processing. This segmentation allows high accuracy during training while maintaining low power consumption during real-time detection.
Solution Approach 2:
The patent performs preliminary processing during a training phase to pre-compute decision trees and attribute relevance scores. By preparing all necessary processing logic in advance, the system eliminates the need for complex real-time calculations during actual scene detection, thereby reducing power consumption while maintaining accuracy.
2Reliability
If multiple sensors are used for scene detection, then detection reliability is improved, but device complexity increases
Solution Approach 1:
The patent extracts and isolates the complexity management to a separate training phase, where multiple sensors and algorithms are coordinated to build decision trees. During execution, only simple attribute value comparisons are needed. This extraction separates the complex setup from the simple runtime operation, reducing perceived device complexity.
Solution Approach 2:
The patent changes the representation of sensor data into discrete decision tree nodes with predefined thresholds. By transforming continuous sensor readings into categorical decisions during training, the system simplifies execution-time processing while maintaining the reliability gained from multiple sensors.
3Speed
If real-time scene detection is implemented, then responsiveness is improved, but processing efficiency decreases
Solution Approach 1:
The patent performs all complex processing during a preliminary training phase to create decision trees and attribute relevance rankings. During real-time detection, the system only needs to traverse these pre-computed trees, achieving both real-time responsiveness and high processing efficiency since no complex calculations are needed at runtime.
Solution Approach 2:
The patent replaces complex real-time algorithmic processing with simple decision tree traversal mechanics. By substituting heavy computational mechanisms with lightweight logical comparisons against pre-stored thresholds, the system achieves real-time performance without sacrificing processing efficiency.
Data Source
AI summary
A method for real-time detection of at least one scene by an apparatus, from among a set of possible reference scenes, includes acquiring current values of attributes from measurement values supplied by sensors. The method further includes traversing a path through a decision tree. The nodes of the decision tree are respectively associated with the attributes. The traversal considers at each node along the path, the current value of the corresponding attribute, so as to obtain at the output of the path, a scene from among the set of reference scenes. The obtained scene identifying which reference scene is the detected scene. The method further includes developing a confidence index (SC) associated with the identification of the detected scene.