Vehicle Object Classification via Image Movement Sequences
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Solution Overview
Problem
Existing systems for identifying objects around a motor vehicle struggle to reliably differentiate between stationary and non-stationary objects using two-dimensional camera images, which can lead to incorrect full-beam regulation and potential dazzling of other road users.
Innovation Solution
A method that estimates the position and movement sequence of objects in image coordinates, comparing these sequences to predetermined characterizing movements to classify objects as stationary or dynamic, allowing for accurate identification without the need for expensive time-of-flight cameras or separate distance measurement sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If two-dimensional camera images are used to identify objects, then the device complexity is reduced, but the reliability of differentiating stationary and non-stationary objects deteriorates
Solution Approach 1:
The patent transforms the problem from spatial differentiation to temporal differentiation by analyzing movement sequences across multiple time points. Instead of relying on additional spatial dimensions or complex sensors, the system captures images at different times and compares the movement patterns of objects relative to the vehicle, enabling reliable classification using simple two-dimensional cameras.
Solution Approach 2:
The patent replaces complex mechanical or optical measurement systems (such as time-of-flight cameras or separate distance sensors) with an image processing system that analyzes temporal sequences of two-dimensional images. The classification is achieved through computational analysis of movement patterns rather than through complex hardware differentiation.
2Measurement precision
If time-of-flight cameras or separate distance measurement sensors are used, then the measurement precision of object position is improved, but the device complexity and cost increase
Solution Approach 1:
The patent creates a virtual model of object movement by capturing and processing sequences of two-dimensional images. Instead of directly measuring three-dimensional position with complex sensors, the system creates a temporal copy of the object's appearance in multiple images and analyzes the movement pattern of this copy to infer stationary or non-stationary status.
Solution Approach 2:
The patent makes the two-dimensional camera multi-functional by using it not only for object detection but also for determining object classification. The same camera system that captures images for basic detection is also used to analyze movement sequences, eliminating the need for separate specialized sensors for distance measurement or object classification.
3Ease of operation
If objects are classified using movement sequences in image coordinates, then the ease of operation is improved, but the measurement precision of object position may deteriorate
Solution Approach 1:
The patent extracts only the essential information needed for classification—the movement sequence of the object in image coordinates—rather than attempting to measure and process complete three-dimensional position data. By taking out only the relevant temporal pattern information, the system achieves simple operation while maintaining sufficient precision for the classification task.
Data Source
AI summary
A method for identifying an object in a surrounding region of a motor vehicle as a stationary object is disclosed. The surrounding region is captured in images using a vehicle-side capture device and the object is detected in the captured images using an image processing device. A first position of the object in the surrounding region relative to the motor vehicle is estimated on the basis of a first captured image, a movement sequence of the object in image coordinates is determined on the basis of the first image and a second captured image, a first movement sequence that characterizes a first, stationary object in the image coordinates is determined proceeding from the first estimated position in the surrounding region, and the captured object is identified as a stationary object on the basis of a comparison of the movement sequence of the captured object to the first characterizing movement sequence.