Rail Crossing Detection Using Fixed Reference Points
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Solution Overview
Problem
Conventional systems for monitoring objects near railroad tracks often generate false positives due to difficulties in distinguishing between objects that should not be on the tracks and those that should, especially under varying lighting conditions and the presence of trains, leading to reduced reliability in detecting rail crossing events.
Innovation Solution
A method and system that process images to identify a plurality of points with a fixed relationship within a region, using these points to determine if an object has entered the region by comparing their presence in successive images, and generating notifications to differentiate between train presence and rail crossing events, thereby reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional vision-based systems are used to monitor objects near railroad tracks, then object detection capability is provided, but false positives increase due to inability to distinguish between objects that should not be on tracks and trains that should be there
Solution Approach 1:
The patent segments the detection task into multiple components: identifying points of interest in the image, tracking their positions across multiple images, and analyzing movement patterns. This segmentation allows the system to distinguish between stationary background objects and moving objects like trains or unauthorized vehicles, reducing false positives while maintaining detection reliability.
Solution Approach 2:
The patent performs preliminary actions by pre-identifying points of interest and their corresponding locations in previous images before detecting object movement. This preliminary mapping of point positions across time enables the system to accurately determine whether an object has crossed the tracks, improving both precision and reliability of detection.
2Area of stationary object
If non-visual sensors are used to detect object movement, then detection coverage is extended, but false positives increase due to inability to distinguish between objects that should not be on tracks and trains
Solution Approach 1:
The patent merges visual-based point identification with movement detection algorithms to create a hybrid system. By combining the area coverage capability of extended detection zones with visual confirmation and movement analysis, the system maintains broad monitoring coverage while significantly reducing false positives through intelligent object differentiation.
3Area of stationary object
If vision-based systems monitor objects in the background, then detection range is increased, but false positives increase due to objects closer to camera appearing larger and seeming to cross tracks
Solution Approach 1:
The patent adds the temporal dimension by tracking points across multiple images over time. This time-based analysis allows the system to distinguish between objects that are merely close to the camera (which will not show consistent movement patterns) and actual crossing events, improving precision while maintaining extended monitoring range.
Data Source
AI summary
A method, data processing system, apparatus, and computer program product for monitoring objects. A plurality of images of an area is received. An object in the area is identified from the plurality of images. A plurality of points in a region within the area is identified from a first image in the plurality of images. The plurality of points has a fixed relationship with each other and the region. The object in the area is monitored to determine whether the object has entered the region. A determination that the object has not entered the region is made in response to identifying an absence of a number of the plurality of points in a second image in the plurality of images.