Object Detection Using Pixel Difference Images for Temporal Analysis
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Solution Overview
Problem
Conventional object detection methods in computer vision struggle to effectively utilize temporal information without altering the structure of machine-learning models, leading to suboptimal performance in identifying objects and actions in image and video analysis.
Innovation Solution
The use of pixel differences between images captured at different times is employed to provide temporal information to machine-learning models, such as convolutional neural networks, allowing them to classify objects and actions without modifying the model structure, and incorporating techniques like binarization and quantization for efficient computation on low-power devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection methods are used without temporal information, then the model structure remains simple, but the detection accuracy is suboptimal
Solution Approach 1:
The patent introduces temporal dimension by incorporating pixel difference images derived from sequential video frames. Instead of modifying the CNN architecture to handle temporal data directly, the solution transforms temporal information into a spatial representation (difference image) that can be processed by the existing single-image detection model, thereby improving accuracy without increasing model complexity
Solution Approach 2:
The patent uses pixel difference images as an intermediary representation that bridges temporal information and the single-image detection model. The difference image serves as a mediator that encodes temporal changes (motion, appearance changes) in a format compatible with conventional object detectors, allowing temporal information to be utilized without altering the detector structure
2Measurement precision
If temporal information is incorporated by modifying model structure, then detection accuracy improves, but computational complexity and power consumption increase
Solution Approach 1:
By transforming temporal information into a spatial difference image, the patent enables processing on low-power devices using standard CNN architectures. This approach avoids the need for complex temporal modeling operations that would increase computational load and power consumption, while still achieving improved detection accuracy through the enriched input representation
Solution Approach 2:
The patent creates a simplified copy of the temporal information in the form of a difference image that captures essential motion and change information. This copied representation can be processed efficiently by existing detectors on resource-constrained devices, avoiding the need to implement and run complex temporal processing models that would consume excessive power
3Measurement precision
If full-resolution pixel difference data is used, then detection accuracy improves, but computational load and processing time increase
Solution Approach 1:
The patent segments the pixel difference computation to focus only on relevant regions. By applying the detection model to the difference image and using the detected object regions to guide further processing or refinement, the system avoids computing and processing pixel differences across the entire high-resolution image, thereby maintaining accuracy while improving processing speed
Solution Approach 2:
The patent computes pixel differences at a reduced resolution or for only the necessary regions of interest rather than processing the full high-resolution image. This partial computation approach provides sufficient temporal information for accurate detection while significantly reducing the computational load and processing time associated with handling complete high-resolution difference data
Data Source
AI summary
Aspects of the subject technology relate to machine learning based object recognition using pixel difference information. A difference image generated by subtraction of a current image from one or more previous images can be provided, as input, to a machine-learning engine. The machine-learning may output a detected object or a detected action based, at least in part, on the difference image. In this way, temporal information about the object can be provided to, and used by, a machine-learning model that is structured to accept image input.