Learning Apparatus for Object Tracking with Variable Frame Intervals
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Solution Overview
Problem
Existing techniques for learning and tracking objects in videos lack efficiency and accuracy, particularly in associating objects between frames with varying time intervals.
Innovation Solution
A learning apparatus and method that acquire a single video, extract sets of frames with different time intervals, detect objects in each frame, associate these objects, and learn an association method based on these associations, improving object tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object association is performed between frames with varying time intervals, then tracking accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies dynamics by making the association mechanism adaptable to varying time intervals between frames. The system dynamically adjusts its association strategy based on the temporal distance between frames, using different association methods for different time intervals. This allows the system to maintain high tracking accuracy across diverse temporal conditions without requiring a single complex fixed approach.
Solution Approach 2:
The patent utilizes parameter changes by varying the association parameters based on time interval characteristics. For different time intervals between frames, the system modifies association parameters such as feature matching thresholds, search windows, and temporal constraints. This enables the system to optimize tracking accuracy for each specific time interval scenario while managing computational complexity through parameter adaptation rather than structural complexity.
2Reliability
If multiple frame sets with different time intervals are used for learning, then learning effectiveness is improved, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the learning process into distinct stages corresponding to different time interval categories. Instead of processing all frame combinations uniformly, the system segments frames into groups based on their temporal relationships and processes each segment with appropriate learning strategies. This segmentation allows the system to leverage diverse time interval data for improved learning effectiveness while managing processing time through structured, modular computation.
Solution Approach 2:
The patent implements periodic action by systematically varying the time intervals between frames in a structured sequence during learning. Rather than randomly processing frames, the system follows a periodic pattern that cycles through different time interval configurations, ensuring comprehensive coverage of temporal variations while maintaining efficient processing rhythms. This periodic approach balances learning effectiveness with processing time management.
Data Source
AI summary
A learning apparatus 1 comprises: an acquisition unit 11 acquires a video MV; an extraction unit 12 extracts sets from frames in the video MV, each of the sets including a first frame and a second frame different from the first frame; a detection unit 13 detects objects in the first and second frames respectively; an association unit 14 associates the object in the first frame with the object in the second frame; and a learning unit 15 makes the association unit 14 learn an association method for the object based on association results by the association unit 14 with respect to the sets, wherein the sets include: a first set where a time interval between the first and second frames is a first interval; and a second set where a time interval between the first and second frames is a second interval different from the first interval.