Object Detection via Target Merging and Tracking
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Solution Overview
Problem
Detecting objects from a moving platform, such as a vehicle, is challenging due to the difficulty in identifying individual objects amidst a large number of candidates within the camera's field of view, especially when both the platform and objects are moving.
Innovation Solution
A method and system that utilize a visual tracking module and detection module to receive and process image frames from cameras, defining targets and determining scores for detected objects, with a merged target being created when the overlap area and score criteria are met, incorporating non-maximal suppression and local motion analysis to enhance object detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If object detection is performed using individual camera frames alone, then the detection process is simple, but the ability to identify individual objects in dynamic environments deteriorates
Solution Approach 1:
The system performs preliminary actions by defining targets in the first image frame and generating predicted targets for the second image frame before actual detection. This preliminary prediction based on tracking processes enables the system to anticipate object locations, making the subsequent detection more accurate in dynamic environments while maintaining operational efficiency.
2Quantity of substance
If multiple candidate objects are present within the field of view, then the detection coverage is comprehensive, but the difficulty of identifying individual objects increases
Solution Approach 1:
The system merges the second target with the first predicted target when their overlap area exceeds a threshold and the second score is greater than the third score. This merging operation consolidates multiple candidate detections into unified object identities, reducing the complexity of identifying individual objects among many candidates while maintaining comprehensive detection coverage.
Solution Approach 2:
The system uses feedback from the tracking process to generate predicted targets that inform the detection process. By comparing detected targets against predicted targets and using score comparisons, the system refines object identification, making it easier to distinguish individual objects even when many candidates are present in the field of view.
3Measurement precision
If tracking process is applied to predict target locations, then the object tracking accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system applies partial action by only performing full tracking and prediction for targets that meet certain criteria (e.g., overlap area threshold, score comparisons). This selective application of the tracking process maintains good target location accuracy while reducing unnecessary computational complexity compared to applying tracking to all detected targets.
Data Source
AI summary
Systems and methods for detecting objects from a moving platform are provided. The method includes receiving, from a camera coupled to the moving platform, first and second image frames, wherein the second image frame is subsequent in time to the first image frame. Within the first image frame, a first target is defined characterizing the location of an object detected in the first image frame. A first score associated with the first target and a second score associated with the second target are determined, and a first predicted target is determined based on a tracking process applied to the first target. A third score associated with the first predicted target is determined. The method determines a merged target corresponding to the second target when the second score is greater than the third score and a proximity measure between the second target and the first predicted target is above a threshold.