Multi-layer Object Segmentation for Occluded ADAS Objects
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Advanced driver-assistance systems (ADAS) face challenges in accurately performing moving object segmentation due to occlusions, which can cause object parts to have irregular shapes, leading to reduced accuracy in object detection and tracking.
Innovation Solution
The proposed solution involves multi-layer object segmentation, where each pixel in a frame is associated with multiple layers, allowing an ADAS to assign each layer to a different class. This enables a single pixel to be assigned to multiple classes, such as moving object, pedestrian, structure, and background, improving the identification and tracking of occluded objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional single-layer segmentation is used, then the system is simple to implement, but occluded objects cannot be accurately identified and tracking accuracy deteriorates
Solution Approach 1:
The patent divides the segmentation task into multiple layers (foreground layer and background layer) where each layer handles different aspects of object detection. The foreground layer identifies visible object parts while the background layer recovers occluded object parts, allowing the system to accurately identify occluded objects without requiring a complete redesign of the entire segmentation system.
Solution Approach 2:
The patent introduces a temporal dimension by using multi-layer segmentation across multiple frames. By associating pixels with multiple layers and using temporal information from previous frames, the system recovers occluded object parts that are not visible in the current frame, thereby improving identification accuracy without significantly increasing spatial complexity.
2Measurement precision
If multi-layer segmentation is implemented, then occluded objects are accurately identified, but computational complexity increases
Solution Approach 1:
The patent segments the segmentation problem into distinct layers (foreground and background) that can be processed independently. This allows the system to achieve high segmentation precision by handling visible and occluded parts separately, while managing computational complexity through modular processing of each layer.
Solution Approach 2:
The system performs preliminary actions by maintaining background layer information from previous frames before new occlusions occur. This pre-computed background information is then used to recover occluded object parts in the current frame, improving measurement precision without requiring intensive real-time computation during critical detection moments.
3Reliability
If each pixel is assigned to a single class, then processing is straightforward, but occluded object parts are missed or misclassified
Solution Approach 1:
The patent segments the classification task by assigning pixels to different layers (foreground/background) rather than forcing a single class assignment. This allows occluded object parts to be correctly classified by the background layer even when foreground objects occlude them, improving detection reliability while maintaining relatively simple classification rules for each layer.
Solution Approach 2:
The patent adds a layer dimension to the classification problem, allowing pixels to belong to different layers with different class assignments. This temporal and hierarchical dimension enables the system to maintain simple classification rules within each layer while achieving reliable detection of occluded objects through multi-layer integration.
4Productivity
If occlusion is not considered, then processing speed is maintained, but temporal inconsistency of object shapes occurs
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing background layer information from frames where objects are not occluded. This pre-computed information is then reused in subsequent frames to maintain consistent object shapes even when occlusions occur, ensuring temporal stability without requiring re-processing of unchanged regions.
Solution Approach 2:
The patent maintains continuity of useful action by continuously updating and maintaining background layer information across frames. This allows the system to preserve object shape consistency over time through continuous integration of background information, while maintaining processing speed by only computing changes rather than re-processing entire frames.
Data Source
AI summary
A system stores frames of data received from a sensor; determines a first class for a first layer for each pixel of a plurality of pixels in a frame of the frames; determines a second class for a second layer for each pixel of the plurality of pixels in the frame; identifies a first object in the frame based on the first class for each pixel of the plurality of pixels; and identifies a second object in the frame based on the first class for each pixel of the plurality of pixels and based on the second class for each pixel of the plurality of pixels, wherein a portion of the plurality of pixels correspond to both the first object and the second object.