Visual Tracking System Using Hybrid Algorithm and Active Platform
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional video surveillance systems face challenges in accurately tracking moving objects due to noise, illumination changes, and rigid camera positioning, leading to incomplete detection and loss of tracking when objects move quickly or are partially occluded, and struggle with complex backgrounds and low image contrast.
Innovation Solution
A visual tracking system that integrates multiple image characteristics using a sensor unit, image processor, hybrid tracking algorithm, and active moving platform, employing binarization, morphological methods, active contour models, fuzzy theory, and visual probability data association filters to maintain accurate tracking even with partial occlusion and low contrast, and compensates for background interference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image subtraction method is used for motion detection, then the system can detect moving objects, but the detection accuracy deteriorates due to camera noise and illumination changes
Solution Approach 1:
The patent combines multiple image characteristics (edge information, gradient direction, intensity changes) into a composite tracking metric. By merging these different features, the system overcomes the limitations of single-feature methods that are sensitive to noise and illumination changes, thereby improving both reliability and precision simultaneously
Solution Approach 2:
The patent introduces an active contour model as an intermediary representation between raw image data and tracking decisions. This model serves as a mediator that filters out noise and illumination variations while preserving essential object characteristics, enabling accurate tracking despite adverse conditions
2Reliability
If template matching method is used for tracking, then the system can track target position, but the tracking accuracy deteriorates under illumination changes and camera noise
Solution Approach 1:
The patent merges multiple image characteristics including edge gradients, contour shapes, and intensity patterns into a multi-dimensional feature space. This combination allows the system to perform template matching that is robust to illumination changes and noise, as the multiple features provide redundant information that compensates for degradation in any single feature
Solution Approach 2:
The patent dynamically adjusts tracking parameters based on image quality metrics. When illumination changes or noise levels increase, the system modifies the weightings of different image characteristics and adjusts the active contour model parameters adaptively, maintaining tracking accuracy across varying conditions
3Device complexity
If fixed camera position is used, then the camera structure is simple, but the viewing angle is constrained and limited
Solution Approach 1:
The patent transitions from a static camera system to a dynamic active camera system that can adjust its viewing angle and position. The camera becomes a movable component that actively tracks targets, allowing the system to maintain simple overall structure while gaining viewing flexibility through controlled camera motion rather than complex multi-camera arrangements
4Productivity
If standard processing speed is used, then the system can process images, but real-time tracking fails when objects move too fast
Solution Approach 1:
The patent performs preliminary extraction of key image characteristics (edges, gradients, contours) before full tracking processing. By pre-processing and identifying critical features in advance, the system reduces the computational burden during real-time tracking, enabling it to keep up with fast-moving objects while maintaining processing quality
Data Source
AI summary
The present invention provides a visual tracking system and its method comprising: a sensor unit, for capturing monitored scenes continuously; an image processor unit, for detecting when a target enters into a monitored scene, and extracting its characteristics to establish at least one model, and calculating the matching scores of the models; a hybrid tracking algorithm unit, for combining the matching scores to produce optimal matching results; a visual probability data association filter, for receiving the optimal matching results to eliminate the interference and output a tracking signal; an active moving platform, for driving the platform according to the tracking signal to situate the target at the center of the image. Therefore, the visual tracking system of the present invention can help a security camera system to record the target in details and maximize the visual information of the intruding target.