Object Trajectory Compression via Segmentation
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Solution Overview
Problem
Existing video-based security systems face challenges in forensic analysis due to the time-consuming and resource-intensive processing of extensive video data for object trajectories, which requires the entire track segment to be present for compression, leading to delays.
Innovation Solution
The method involves compressing an initial set of track points into a plurality of track segments with start and end points, using algorithms like 'Opening Window' and eigen value-based methods to reduce data while maintaining error within predetermined spatial and velocity thresholds, allowing for efficient storage and retrieval of object trajectories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire video data is processed to compute object trajectories, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent divides the complete trajectory into multiple segments based on spatial and velocity thresholds. Each segment represents a portion of the trajectory between specific track points, allowing the system to process and store only the essential segments rather than the entire trajectory data, thus reducing processing time while maintaining accuracy.
Solution Approach 2:
The patent extracts and stores only the critical track points and segments that meet predetermined spatial and velocity thresholds, rather than storing the complete trajectory data. This extraction approach removes redundant information while preserving the essential characteristics needed for forensic analysis.
2Measurement precision
If cumulative error computation method is used for data compression, then measurement precision is maintained, but device complexity increases
Solution Approach 1:
The patent performs compression actions during the trajectory processing stage by pre-determining segment boundaries based on spatial and velocity thresholds. This preliminary segmentation avoids the need for complex cumulative error computations during later analysis stages, reducing overall system complexity while maintaining precision.
Data Source
AI summary
Methods, devices, and systems for object tracking are described herein. One or more method embodiments include receiving an initial set of track points associated with a trajectory of an object, compressing the initial set of track points into a plurality of track segments, each track segment having a start track point and an end track point, and storing the plurality of track segments to represent the trajectory of the object.