Object Tracking Search Range Optimization via Movement History
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Solution Overview
Problem
Existing object tracking methods in video images face increased detection errors when the object's movement deviates from initial assumptions, leading to higher calculation costs to maintain tracking accuracy.
Innovation Solution
An image processing apparatus that generates a movement history of objects by aggregating and clustering their movement destinations across a grid-divided image area, allowing for a reduced search range and prioritization of search areas to minimize detection errors while reducing processing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the search area is enlarged or prediction time intervals are shortened to reduce detection errors, then tracking accuracy is maintained, but calculation amount of processing increases
Solution Approach 1:
The image area is divided into multiple grid cells, and the search area is further segmented into priority regions (high, medium, low) based on movement history. This segmentation allows the system to focus computational resources on high-probability search regions rather than uniformly processing the entire search area, thereby reducing calculation amount while maintaining tracking accuracy.
Solution Approach 2:
Different search regions are assigned different quality levels based on movement history analysis. High-priority regions (where objects are most likely to appear) receive intensive search processing, while low-priority regions receive reduced processing. This local quality differentiation maintains detection accuracy in critical areas while reducing overall computational load.
2Reliability
If the search area is enlarged to reduce detection errors, then tracking accuracy is maintained, but processing time increases
Solution Approach 1:
The system performs preliminary analysis of movement history to predict the most likely search regions before actual object detection. By pre-identifying high-priority search areas based on aggregated object movement patterns, the system avoids wasting processing time on low-probability regions while ensuring adequate coverage of high-probability areas, thus reducing overall processing time without compromising detection accuracy.
Solution Approach 2:
The system creates a simplified representation (copy) of the search problem by generating a priority map from movement history. This priority map serves as a guide that directs the actual detection process to focus on specific high-probability regions, effectively copying the essential information needed for accurate detection while avoiding unnecessary processing of irrelevant areas.
3Reliability
If the prediction time intervals are shortened to reduce detection errors, then tracking accuracy is maintained, but calculation amount of processing increases
Solution Approach 1:
The system dynamically adjusts the prediction time interval based on object characteristics and movement patterns. For objects with stable, predictable movement, longer time intervals are used to reduce processing frequency. For objects with erratic or changing movement patterns, the system automatically shortens the interval. This dynamic adaptation maintains tracking accuracy for all object types while optimizing computational resource usage.
Data Source
AI summary
An image processing apparatus includes an image acquisition unit configured to extract an image from a video image, a history management unit configured to generate movement history of an object, a setting unit configured to set a search range in the image based on the movement history, and a tracking unit configured to detect a target object in the search range and associate a currently detected target object with a previously detected target object, to perform tracking on the target object.