Image Target Tracking via Sparse Sampling and Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional image target tracking schemes struggle with accurate tracking when targets are blocked by camouflage, leading to erroneous tracking of other objects, especially in complex environments like crowds.
Innovation Solution
An image target tracking method and system that employs sparse sampling, clustering, and robust estimation to differentiate between target and camouflage interference, generating a prediction trajectory and correlation result to determine if the target is blocked and for accurate tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image target tracking scheme is used, then tracking speed is maintained, but tracking accuracy deteriorates when target is blocked by camouflage
Solution Approach 1:
The patent segments the target and camouflage regions by dividing sampling points into foreground and background sets, then performs separate clustering on each set to identify distinct spatial distributions. This segmentation allows the system to differentiate between target pixels and camouflage interference pixels, resolving the tracking accuracy issue when targets are blocked by camouflage.
Solution Approach 2:
The patent introduces robust estimation as an intermediary mechanism that analyzes the spatial distribution characteristics of foreground and background sampling points. This intermediary process generates a prediction trajectory that mediates between observed samples and actual target position, enabling accurate target localization even when directly obscured by camouflage.
2Measurement precision
If sparse sampling and clustering are performed to improve tracking accuracy, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The algorithm segments the computational process into distinct phases: sparse sampling to generate initial candidate points, division into foreground/background sets, and separate clustering operations. This segmentation reduces the overall computational complexity by breaking down the complex task of target identification in camouflage into manageable sub-tasks that can be processed efficiently.
Solution Approach 2:
The patent applies partial action by performing sparse sampling rather than exhaustive sampling of the entire image. By selecting only a representative subset of sampling points and applying clustering only to relevant foreground points, the algorithm achieves sufficient tracking accuracy with reduced computational effort compared to processing all image pixels.
Data Source
AI summary
An image target tracking method and system thereof are provided in the present disclosure. The image target tracking method includes the following steps: obtaining a target initial position, and performing a sparse sampling according to the target initial position; dividing sampling points into foreground sampling points and background sampling points; clustering adjacent foreground sampling points according to a spatial distribution of the foreground sampling points in order to obtain a clustering result containing a plurality of clusters; performing a robust estimation according to the clustering result in order to determine a relative position between a target and a camouflage interference in an image; and generating a prediction trajectory, correlating an observation sample position with the prediction trajectory to generate a correlation result, and determining whether the target is blocked and tracking the target according to the correlation result.