Particle Tracking Penalty Graph Hijacking Prevention
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Solution Overview
Problem
Conventional methods for tracking multiple image objects often result in hijacking phenomena where nearby objects' identifiers are confused or lost, leading to incorrect tracking and replacement of identification information.
Innovation Solution
A method that calculates weights based on distances between adjacent image objects using a penalty graph, preventing objects from being recognized as a single entity and ensuring accurate identification by assigning penalties inversely proportional to the distance between particles and neighboring objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional tracking methods are used based on previous object location, then tracking simplicity is maintained, but identification information of adjacent objects is confused or lost
Solution Approach 1:
The patent divides the tracking space by introducing a penalty graph that segments the probability distribution of particle locations. When objects are adjacent, the penalty graph creates distinct regions with different penalty values, effectively segmenting the tracking space to prevent identifier confusion between objects
Solution Approach 2:
The penalty graph acts as an intermediary mechanism between particles and objects. It introduces penalty values that mediate the association between particles and objects, preventing direct misassociation when objects are close together by incorporating distance-based penalty calculations
2Device complexity
If particles are resampled without penalty consideration, then tracking computation is simpler, but hijacking phenomenon occurs where single object incorporates multiple identification information
Solution Approach 1:
The penalty graph is constructed in advance before particle resampling, and penalty values are pre-calculated based on object distances. This preliminary action prevents hijacking by establishing constraint conditions before the resampling process occurs, rather than correcting issues after they arise
Solution Approach 2:
The patent changes the weight parameter of particles by multiplying with penalty values during the resampling process. This parameter modification ensures that particles near object boundaries receive appropriate penalty adjustments, preventing them from incorrectly associating with multiple objects
Data Source
AI summary
A method for tracking multiple image objects, includes resampling particles from each of the image objects tracked in a previous image, calculating respective weights of the resampled particles, and predicting and tracking locations of the image objects in a current image based on values obtained by multiplying the locations of the particles by corresponding weights, wherein when an image object of the previous image has a neighboring image object located within a threshold distance, weights of the particles sampled from the image object are calculated by multiplying image values of the current image by penalties, which are values of distances between the corresponding particles and the neighboring image object. The method is capable of avoiding a hijacking problem in which identifiers of nearby objects are confused with each other or disappear during the tracking of multiple objects.