Hierarchical Snapshot Sampling for Efficient Object Re-Identification
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Solution Overview
Problem
Surveillance systems face computational challenges in accurately tracking and re-identifying objects across multiple cameras due to the high computational intensity of existing descriptor extraction methods.
Innovation Solution
A hierarchical sampling method that clusters snapshots based on similarity and extracts descriptors of varying complexity levels, reducing the number of snapshots while maintaining re-identification accuracy by using neural networks to estimate snapshot quality and select representative frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex descriptors are applied to all snapshots for accurate re-identification, then identification accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the snapshot processing into two distinct groups: representative snapshots that receive complex descriptor processing, and non-representative snapshots that use simpler methods. This segmentation is achieved through clustering snapshots based on similarity metrics, selecting one representative from each cluster, and applying computationally intensive complex descriptors only to these representatives while using simpler descriptors for the remaining snapshots.
2Measurement precision
If all snapshots are processed with high computational methods, then identification accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary clustering and representative snapshot selection before applying complex descriptor processing. By pre-identifying which snapshots are representative based on simpler similarity metrics, the system prepares the data structure in advance, allowing complex computations to be focused only on the necessary subset of snapshots, thereby reducing overall processing time while maintaining accuracy.
3Productivity
If the number of snapshots processed is reduced for efficiency, then computational cost decreases, but identification accuracy may deteriorate
Solution Approach 1:
The patent applies local quality by differentiating the processing quality applied to different snapshots. Representative snapshots, which are deemed most informative for identification, receive high-quality complex descriptor processing, while non-representative snapshots use simpler, lower-quality processing. This localized quality assignment ensures that computational resources are concentrated on the most critical snapshots, maintaining overall identification accuracy while improving efficiency.
Data Source
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AI summary
Aspects of the present disclosure include methods, systems, and non-transitory computer readable media that perform the steps of receiving a first plurality of snapshots, generating a first plurality of descriptors each associated with the first plurality of snapshots, grouping the first plurality of snapshots into at least one cluster based on the plurality of descriptors, selecting a representative snapshot for each of the at least one cluster, generating at least one second descriptor for the representative snapshot for each of the at least one cluster, wherein the at least one second descriptor is more complex than the first plurality of descriptors, and identifying a target by applying the at least second descriptor to a second plurality of snapshots.