Object Tracking With Adaptive Frame Sampling for Long Video Streams
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Solution Overview
Problem
Existing object tracking approaches are computationally expensive and inefficient, especially for long videos or real-time applications, due to the need to process every frame, leading to suboptimal tracking performance and missed object movements.
Innovation Solution
Non-uniformly sampling frames at varying time intervals, using techniques such as random, fixed function, or adaptive sampling, to focus computational resources on more informative frames, thereby reducing the number of frames processed and improving tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If every frame is processed to detect and track objects, then tracking accuracy is improved, but computational cost increases
Solution Approach 1:
The patent applies partial action by processing only a subset of frames rather than every frame. It uses selective frame sampling where frames are chosen based on their informational value for tracking, processing fewer frames while maintaining tracking accuracy through intelligent selection of which frames to analyze.
Solution Approach 2:
The patent changes the parameter of frame selection from uniform processing of all frames to selective processing based on frame importance metrics. It dynamically adjusts which frames are processed by evaluating their contribution to tracking accuracy, thereby reducing computational cost while preserving measurement precision.
2Productivity
If frame sub-sampling is used to reduce computational cost, then processing speed is improved, but tracking performance deteriorates
Solution Approach 1:
The patent changes the sampling parameter from fixed uniform sub-sampling to adaptive sampling based on frame importance. Frames are selected for processing based on metrics that evaluate their informational value for tracking, ensuring that processing speed is improved while tracking performance is maintained through intelligent frame selection.
Solution Approach 2:
The patent applies local quality by treating different frames differently based on their specific characteristics and importance for tracking. Rather than uniform sub-sampling, it selectively processes frames that are most informative for tracking objectives, allocating computational resources locally to where they provide maximum tracking performance benefit.
3Power
If fixed interval sub-sampling is applied, then computational burden is reduced, but important object motion is missed
Solution Approach 1:
The patent changes the sampling interval parameter from fixed to variable based on frame importance metrics. Instead of processing every nth frame, it dynamically determines which frames to process based on their contribution to capturing object motion, reducing computational burden while preventing loss of important motion information through adaptive frame selection.
Solution Approach 2:
The patent implements feedback by evaluating the importance of each frame based on tracking objectives and object motion characteristics. This feedback mechanism guides the selection of frames to process, ensuring that frames containing important object motion are captured while minimizing computational burden through intelligent, adaptive frame sampling.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for performing object detection in a sequence of frames, including: sampling a plurality of frames from the sequence of frames, wherein at least two pairs of frames that are adjacent in time in the plurality of frames are separated by different time intervals; inputting the plurality of frames into a first machine learning model trained to track objects; and obtaining as output from the first machine learning model, based on the input plurality of frames, at least one of an identity or location corresponding to one or more objects in the plurality of frames.