Single-Line Video Processing for Directional Object Counting
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Solution Overview
Problem
Existing computer vision methods for extracting moving objects from video data are computationally expensive, especially when dealing with constrained motion scenarios like roads or corridors, due to the need to process each pixel in each frame to update the background model.
Innovation Solution
The method transforms video data into directional object counts by isolating a single column or row of pixels from each image, applying one-dimensional background subtraction, and using techniques like quasi-connected components and scale-invariant feature transformation to determine object location and direction, reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional background subtraction methods are used to extract moving objects from video frames, then object detection accuracy is maintained, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the video processing task by isolating and processing only specific columns or rows of pixels that are relevant to detecting objects in constrained motion scenarios, rather than processing the entire video frame. This segmentation reduces the data volume while maintaining detection accuracy for the specific application.
Solution Approach 2:
The patent extracts only the necessary portion of video data (specific columns or rows of pixels) that contain information about objects moving in constrained directions, discarding the rest of the frame data. This extraction approach maintains object detection capability while significantly reducing computational requirements.
2Reliability
If full video frame processing is performed to account for all object motion variables, then comprehensive object tracking is achieved, but processing speed and energy efficiency decrease
Solution Approach 1:
The patent applies local quality by processing only specific regions (columns or rows) of the video frame that are relevant to the constrained motion scenario, rather than uniformly processing the entire frame. This allows the system to maintain tracking reliability for the specific application while improving processing speed.
Solution Approach 2:
The patent performs partial action by processing only a subset of the video data (specific columns or rows) rather than the complete frame, which is sufficient for detecting objects in constrained motion scenarios. This partial processing achieves the necessary tracking reliability while significantly improving processing speed.
3Measurement precision
If standard background subtraction is applied to each pixel in every frame, then temporal changes are accurately captured, but energy consumption and computational resources increase
Solution Approach 1:
The patent segments the pixel processing task by applying background subtraction only to selected columns or rows of pixels rather than all pixels in the frame. This segmentation maintains accurate temporal change detection for the relevant regions while reducing energy consumption proportionally to the reduction in processed pixels.
Solution Approach 2:
The patent performs partial background subtraction on only a subset of pixels (specific columns or rows) that are sufficient for detecting temporal changes in constrained motion scenarios. This partial action maintains detection accuracy while significantly reducing the energy and computational resources required.
Data Source
AI summary
The present invention is a computer-implemented system and method for transforming video data into directional object counts. The method of transforming video data is uniquely efficient in that it uses only a single column or row of pixels in a video camera to define the background from a moving object, count the number of objects and determine their direction. By taking an image of a single column or row every frame and concatenating them together, the result is an image of the object that has passed, referred to herein as a sweep image. In order to determine the direction, two different methods can be used. Method one involves constructing another image using the same method. The two images are then compared, and the direction is determined by the location of the object in the second image compared to the location of the object in the first image. Due to this recording method, elongation or compression of the objects can occur because of acceleration or deceleration of the objects and can be uniquely utilized to determine the speed or movement path of the objects. The second method of determining direction involves comparing the object in the image to an established marker. The transformations can also be used to produce labeled data for training machine learning models; bounding-boxes provided in sweep image can be transformed to bound boxes in video, and boxes in video can be transformed into boxes in the sweep image.