Trajectory Waypoint Scoring for Action Image Selection
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Solution Overview
Problem
Existing digital imaging systems struggle to effectively select key images from action sequences, as current techniques like face detection and motion detection are not well-suited for capturing moments of change, such as the beginning, apex, and landing of an action, leading to the retention of blurry or uninteresting images.
Innovation Solution
A computer-implemented method for image processing that involves detecting objects in an image sequence, determining their trajectories, calculating trajectory waypoint scores, and aggregating these scores to select images that represent key moments of action, such as changes in direction, ensuring sharper and more relevant images are chosen.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If face detection and motion detection techniques are used to select images from an image sequence, then the selection process is simplified and can be performed automatically, but the selected images may not accurately represent key action moments and may include blurry or uninteresting images
Solution Approach 1:
The patent changes the detection parameters from simple face/motion presence to trajectory-based motion pattern analysis. By tracking object positions across multiple images and analyzing trajectory characteristics (direction changes, acceleration patterns), the system achieves both automated processing and accurate identification of key action moments such as jumps, throws, and catches.
Solution Approach 2:
The patent adds a temporal dimension to image selection by analyzing sequences of images rather than individual frames. By examining trajectory data across multiple time points and calculating motion patterns, the system can identify key moments that simple motion detection would miss, thereby improving accuracy while maintaining automation.
2Reliability
If a large number of images are captured to ensure key moments are included, then the probability of capturing action moments increases, but the amount of data to process and store increases significantly
Solution Approach 1:
The patent extracts only the essential information needed for action detection by analyzing trajectory patterns and motion characteristics rather than processing all captured images equally. By identifying key moments through trajectory analysis, the system can selectively retain only the most relevant images, reducing the quantity of stored images while maintaining high reliability of capturing key actions.
Solution Approach 2:
The patent performs preliminary analysis of image sequences to identify key action moments before final image selection and storage. By pre-processing the sequence to detect trajectory patterns and motion changes, the system can determine which images are most valuable to retain, thereby reducing the total number of images that need to be processed and stored while ensuring key moments are captured.
3Ease of manufacture
If existing motion detection techniques are used, then the implementation is straightforward and computationally efficient, but the system cannot distinguish between meaningful action changes and routine motion
Solution Approach 1:
The patent introduces trajectory analysis as an intermediary layer between simple motion detection and complex action recognition. By first detecting object motion and then analyzing trajectory patterns (direction changes, acceleration, deceleration), the system maintains implementation simplicity while gaining the ability to distinguish meaningful action changes from routine motion through the intermediate trajectory evaluation step.
Data Source
AI summary
The disclosure pertains to techniques for image processing. One such technique comprises a method for image selection, comprising obtaining a sequence of images, detecting one or more objects in one or more images of the sequence of images, determining a location for each detected object in the one or more images, determining a trajectory for each detected object based on a determined location for each respective detected object in two or more images of the sequence of images, determining a trajectory waypoint score for the trajectory of each detected object, determining a set of selected images based on an aggregation of trajectory waypoint scores for each detected object in each respective image, and outputting the set of selected images for presentation.