Trajectory-Based Object Detection for Real-Time Imaging
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Solution Overview
Problem
Conventional image processing techniques for object detection in a field of view are cumbersome, processing-intensive, and unreliable due to issues like image occlusion and defragmentation, making them unsuitable for real-time applications.
Innovation Solution
A method and system that define a trajectory within a target scene, extract a subset of pixel values corresponding to this trajectory from multiple images, and process these values to detect objects, reducing the need for background learning and complex classification systems, thus enabling efficient and rapid object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general subtraction methods are used to detect objects in a scene, then object detection can be performed, but processing overhead is excessive and real-time processing is not achieved
Solution Approach 1:
The patent segments the image processing task by dividing the scene into multiple regions of interest (ROIs) based on expected object locations. Instead of processing the entire image, the system focuses computational resources only on specific areas where objects are likely to appear, thereby reducing processing overhead while maintaining detection accuracy.
Solution Approach 2:
The patent applies different processing qualities to different parts of the image. High-precision processing is applied only to regions where objects are expected to appear, while other areas receive minimal or no processing. This local differentiation optimizes the balance between detection accuracy and processing speed.
2Reliability
If conventional image processing techniques are used, then object detection is possible, but the system is cumbersome and processing-intensive
Solution Approach 1:
The patent performs preliminary actions by pre-defining regions of interest and pre-processing steps before actual object detection. Background subtraction and ROI identification are performed in advance, allowing the main detection algorithm to operate on simplified data, thereby reducing system complexity while maintaining reliability.
Solution Approach 2:
The patent extracts only the necessary components for object detection by isolating regions of interest from the full image. By taking out and processing only the relevant portions of the image data, the system reduces complexity while maintaining detection reliability.
3Measurement precision
If full image processing is performed to detect objects, then comprehensive detection is achieved, but processing time increases excessively
Solution Approach 1:
The patent divides the image into multiple segments or regions of interest, processing each segment independently and in parallel. This segmentation allows the system to maintain high detection accuracy within each region while significantly reducing the total processing time compared to analyzing the entire image sequentially.
Solution Approach 2:
The patent applies partial processing by focusing computational effort only on regions where objects are likely to appear, rather than processing the entire image uniformly. This selective approach reduces processing time while maintaining detection accuracy in the critical regions.
Data Source
AI summary
Systems and methods according to one or more embodiments are provided for detecting an object in a field of view of an imaging device. An object may be detected by an imaging device when the object is present along a trajectory in a target scene. In one example, a system includes a memory component to store a plurality of images of the target scene and a processor. The processor is configured to define the trajectory between two locations within the target scene and extract a subset of pixel values from each of successive images corresponding to the trajectory. The extracted subsets of pixel values are processed to detect an object within the target scene. Additional systems and methods are also provided.