Image Sensor Statistics Processing for Low-Power Event Detection
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Solution Overview
Problem
Implementing image processing functionality on resource-constrained devices and apparatuses is challenging due to the high resource requirements of generating and processing image sensor data, which can quickly drain power and memory, especially in low-power applications like home security cameras and battery-powered devices.
Innovation Solution
A method and apparatus that utilize statistics data modules to generate statistics data from image sensor data, allowing event detection without fully processing the entire image frame, thereby reducing resource consumption and enabling efficient event detection in low-power modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full image sensor data processing is implemented, then event detection accuracy is improved, but power consumption and resource usage increase significantly
Solution Approach 1:
The patent divides image processing into two segments: a first processing stage that generates statistics data (e.g., mean, variance, histograms) from image sensor data, and a second processing stage that performs full event detection. By segmenting the processing pipeline, the system can operate in a low-power mode using only statistics data while maintaining the capability for accurate event detection when needed.
Solution Approach 2:
The patent applies partial action by processing only statistical features of image data rather than complete pixel-level analysis. The statistics data module computes condensed representations (such as mean intensity, variance, and histogram distributions) that capture essential scene characteristics without requiring full image processing, thereby reducing computational load and power consumption while still enabling effective event detection.
2Reliability
If full image sensor data is processed continuously, then scene monitoring reliability is improved, but resource consumption increases
Solution Approach 1:
The patent extracts essential statistical features from image sensor data using a dedicated statistics data module. This extraction process isolates the most relevant information (mean, variance, histogram data) needed for event detection, separating it from the full image data. This allows the system to maintain monitoring reliability using only the extracted statistics, significantly reducing resource consumption.
3Use of energy by moving object
If statistics data processing is used, then power consumption is reduced, but event detection capability may be compromised
Solution Approach 1:
The patent changes the parameter representation from full pixel data to statistical parameters (mean, variance, histogram bins). By transforming the data into these condensed statistical forms, the system reduces computational requirements and power consumption while preserving the essential information needed for event detection. The statistics capture scene characteristics efficiently, maintaining detection capability at lower power levels.
Data Source
AI summary
The present disclosure relates to a method of processing image data at an apparatus having an image sensor, a first statistics data module and a first processor component, the method comprising: obtaining, at the first statistics data module from the image sensor, first image sensor data; generating, at the first statistics data module, statistics data of a first type derived, at least in part, from the first image sensor data; processing, at the first processor component, the statistics data of the first type to determine whether or not an event is detected in a scene; generating, at the first processor component, an event signal when an event is detected.