Synthetic Pixel Generation for Real-Time Occlusion Removal
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Solution Overview
Problem
Real-world scenarios often present occlusions that hinder the effective detection and tracking of objects by computer vision systems, leading to inaccuracies and errors such as double counting.
Innovation Solution
The implementation of a real-time occlusion removal system using synthetic pixel generation, where a custom neural network generates synthetic versions of occluded objects, allowing computer vision systems to accurately detect and track objects even when partially or fully occluded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If computer vision systems process real-world video feeds with occlusions, then object detection and tracking can be performed, but detection accuracy and tracking reliability deteriorate due to occluded objects
Solution Approach 1:
The system performs preliminary occlusion detection by comparing current frame objects with tracked object histories before final detection. By proactively identifying occluded objects and generating synthetic data to supplement missing information, the system prepares compensation data in advance, preventing accuracy degradation during occlusion events
Solution Approach 2:
Synthetic object data acts as an intermediary between the occluded real-world input and the detection system. The system generates synthetic representations of occluded objects based on historical tracking data and physical constraints, inserting these synthetic intermediaries into the detection pipeline to maintain continuous object presence information without relying solely on blocked visual input
2Measurement precision
If computer vision systems attempt to track objects through occlusions using only real visual data, then tracking continuity may be maintained, but detection precision and object identification accuracy worsen due to incomplete visual information
Solution Approach 1:
The system creates synthetic copies of occluded objects based on their known properties, historical positions, and physical constraints. These synthetic copies serve as data replacements for the invisible portions of occluded objects, allowing the detection system to maintain accurate object identification by referencing complete synthetic representations rather than incomplete real visual data
Solution Approach 2:
The system implements feedback loops where detection results and tracking information continuously inform synthetic data generation. By feeding back tracked object histories, detection confidence levels, and physical constraint models into the synthetic data generation process, the system iteratively refines synthetic object representations to improve identification accuracy while accounting for information loss during occlusion
Data Source
AI summary
Systems and methods described herein utilize synthetic pixel generation using a custom neural network to generate synthetic versions of objects hidden by occlusions for effective detection and tracking. A computing device stores an object detector model and a synthetic image generator model; receives a video feed; detects objects of interest in a current frame of the video feed; identifies an occluded object in the current frame; retrieves a previous frame from the video feed; generates synthetic data based on the previous frame for the occluded object; and forwards a modified version of the current frame to an object tracking system, wherein the modified version of the current frame includes the synthetic data.