Object Tracking Device Grouping for Reduced Computational Load
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing object tracking systems face increased workload when processing a large number of objects in a time series, particularly in pedestrian detection and driving support devices, which can lead to inefficiencies and potential safety issues.
Innovation Solution
An object tracking method that groups detected objects based on their influence on the movement of a host vehicle, allowing each group to be tracked as a single object in a time series, thereby reducing the computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all detected objects are tracked individually in a time series, then tracking precision is improved, but calculation workload increases
Solution Approach 1:
The patent merges multiple detected objects into a single tracking target when they satisfy specific conditions: same detection box ID, distance between centers less than threshold, and similar movement directions. This combining approach reduces the number of individual tracking operations while maintaining accurate tracking of the merged group, thereby decreasing calculation workload without significantly compromising tracking precision.
Solution Approach 2:
The patent segments the tracking process into two distinct phases: object detection phase and object tracking phase. In the detection phase, multiple objects are identified individually with high precision. In the tracking phase, these detected objects are evaluated against merging conditions, and only those that meet the criteria are combined. This segmentation allows the system to maintain detection precision while reducing tracking complexity through selective merging.
2Measurement precision
If all detected objects are tracked individually in a time series, then tracking accuracy is improved, but processing time increases
Solution Approach 1:
By merging multiple objects that move together and satisfy the specified conditions into a single tracking target, the patent reduces the total number of tracking operations required. This directly decreases processing time while maintaining tracking accuracy for the merged group, as the system tracks the collective movement rather than individually processing each object within the group.
Solution Approach 2:
The patent applies partial merging rather than merging all detected objects. Only objects that satisfy the merging conditions (same box ID, close distance, similar direction) are combined, while others remain as individual tracking targets. This selective partial action optimizes processing time by reducing redundant calculations only where appropriate, without sacrificing tracking accuracy for objects that should be tracked separately.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An object tracking device tracks objects (8) in a time series using an object detection sensor (10) for detecting objects (8) around a host vehicle (7). When a plurality of objects (8) are detected, the object tracking device sorts the objects (8) into groups (G) in accordance with a degree of influence of the positions of the objects (8) on the movement (9) of the host vehicle (7). The object tracking device tracks each of the groups (G) as a single object in a time series.