Image Processing Device for High-Speed Target Tracking
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Solution Overview
Problem
Conventional methods for detecting and tracking specific targets from high-resolution images require high computational power, leading to increased device size and power consumption, and are unable to simultaneously monitor multiple targets due to high processing loads.
Innovation Solution
The proposed solution involves a video acquisition unit, an object detection processing unit, a tracking processing unit, and an integration processing unit that acquire and process high-resolution images, with object detection performed on the full image and tracking on a resized image, allowing for high-speed and high-precision tracking while reducing processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object detection processing is performed on high-resolution images to achieve high precision tracking, then measurement precision is improved, but use of energy and device complexity increase
Solution Approach 1:
The patent segments the image processing into two distinct stages: first, object detection is performed on the full high-resolution image to identify target objects; second, tracking processing is performed on a cropped, lower-resolution version of the target region. This segmentation allows the system to maintain high tracking precision while reducing the computational energy required for continuous processing.
Solution Approach 2:
The patent extracts only the necessary portion of the high-resolution image (the target object region) for tracking processing. By cropping the image to contain only the detected target and its surrounding area, the system reduces the amount of data that needs to be processed continuously, thereby reducing power consumption while maintaining tracking accuracy.
2Measurement precision
If object detection processing is performed on high-resolution images to achieve high precision tracking, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the processing system into two functional modules: an object detection module that processes the full high-resolution image, and a tracking module that processes the cropped lower-resolution image. This segmentation of processing tasks simplifies the overall system architecture by clearly defining the responsibilities of each module and reducing the computational burden on any single component.
Solution Approach 2:
The patent extracts and processes only the essential information for tracking (the cropped target region) separately from the full image processing. This extraction approach simplifies the tracking module's computational requirements and allows the system to use less complex hardware while maintaining high tracking precision.
3Productivity
If image size is reduced to improve processing speed, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The patent segments the processing into two stages with different resolution requirements: initial object detection on full-resolution images maintains high detection precision, while subsequent tracking on cropped lower-resolution images achieves high processing speed. This segmentation allows the system to optimize for precision where needed and for speed where appropriate.
Solution Approach 2:
The patent performs preliminary object detection on the full high-resolution image before performing tracking processing. By first identifying the target objects in the complete image, the system can then crop and process only the relevant regions at lower resolution, ensuring that no targets are missed while achieving fast tracking processing.
4Reliability
If full image processing is performed to detect multiple targets, then reliability is improved, but use of energy increases
Solution Approach 1:
The patent segments the processing workload by performing comprehensive object detection on the full image only when needed to identify multiple targets, and then performing energy-efficient tracking on cropped regions for each detected target. This segmentation allows the system to maintain reliable detection of multiple targets while reducing overall power consumption compared to continuous full-image processing.
Data Source
AI summary
The present disclosure provides an image processing technique for performing high-speed tracking of a detection target in wide area monitoring. An image processing device includes a video acquisition unit to acquire a first image, an object detection processing unit to execute an object detection process on the first image, specify a target object therein, and generate a first object detection result indicating a position of the target object in the first image, a tracking processing unit to acquire a target region image including the target object, generate a resized image by executing a resizing process, execute an object detection process on the resized image, and generate a second object detection result indicating a position of the target object therein, and an integration processing unit to generate a final object detection result by integrating the first and second object detection results.