Sample-Mean Data Clustering for Low-Memory Video Frame Analysis
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Solution Overview
Problem
Existing video processing techniques for identifying specific elements, such as human faces, are time-consuming and resource-intensive, particularly in applications like security and streaming video, due to the large number of frames that need to be analyzed and processed.
Innovation Solution
A clustering method that assigns video frames to samples, calculates mean values for each sample, and uses these summaries to reduce memory consumption and processing time by maintaining only sample means, rather than individual observation data, while dynamically determining when to create new clusters or merge existing ones based on distance thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all video frames are stored and processed individually for clustering analysis, then clustering accuracy is improved, but memory consumption increases significantly
Solution Approach 1:
The patent creates a simplified copy of the cluster data in memory (cluster data structure with aggregated statistics) that represents the essential characteristics of the full video frame data. This copy allows clustering operations to be performed on compressed representations rather than the complete original data, significantly reducing memory requirements while maintaining clustering functionality
Solution Approach 2:
The patent transforms the data representation from storing complete video frames to storing aggregated statistical parameters (means, standard deviations, frame counts) that characterize the cluster. This parameter transformation enables the system to work with compact numerical summaries instead of large image data, resolving the memory vs. accuracy contradiction
2Measurement precision
If thousands of video frames are processed individually, then identification accuracy is improved, but processing time increases significantly
Solution Approach 1:
The patent segments the video processing task into two distinct phases: an offline clustering phase that processes all frames to build cluster models, and an online identification phase that uses these models for rapid classification. This segmentation allows computationally intensive operations to be performed once offline, while online processing becomes much faster
Solution Approach 2:
The patent performs preliminary clustering analysis offline to pre-compute cluster centers, means, and standard deviations from all video frames. This preliminary action creates ready-to-use cluster models that can quickly classify new frames without repeating the full analysis, significantly reducing online processing time while maintaining accuracy
3Measurement precision
If complete observation data is maintained for all classified observations, then clustering precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts only the essential statistical features (means and standard deviations) from the complete observation data and stores these extracted features in the cluster data structure. By taking out only the necessary information for clustering operations, the system reduces data management complexity while preserving clustering precision
Data Source
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AI summary
Techniques and solutions are described for clustering, with reduced memory consumption, observations in a data set. A first observation is received. A distance is determined between the first observation and a first cluster. It is determined that the distance satisfies a threshold, and the first observation is added to a first sample of the first cluster. A first set of means is calculated for the first sample using data of the first observations and data of at least a second observation of the first sample. A second set of means is calculated for the plurality of elements for the first cluster using the first set of means for the first sample and at least a third set of means for at least a second sample of the first cluster. An identifier of the first observation is added to a collection of observation identifiers classified in the first cluster.