PTZ Tracking Target Estimation for Manual-to-Auto Mode Switching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing monitoring systems with PTZ cameras face challenges in operator operability, including the need for multiple cameras, costly control systems, and difficulties in accurate detection of moving objects from afar, especially when switching between manual and automatic tracking modes, which can lead to increased workload and loss of tracking opportunities.
Innovation Solution
A computational processing device and method that estimates a tracking target during manual tracking, allowing seamless switching to automatic tracking by specifying the estimated target as the tracking target for the automatic mode, using a tracking target estimation unit to overlay frames on the display, and enabling a simple one-action mode switch.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to improve detection accuracy, then detection precision is improved, but device complexity and cost increase
Solution Approach 1:
The system performs preliminary actions by detecting moving objects and creating abstracted images before the operator needs to specify a tracking target. The computational processing device continuously monitors the monitoring region image, detects moving objects, and prepares abstracted images in advance, so that when automatic tracking is activated, the tracking target can be immediately specified from the pre-detected objects without requiring multiple cameras or complex control systems.
2Ease of operation
If the operator manually specifies tracking targets, then operability is improved, but loss of time occurs during mode switching
Solution Approach 1:
The system performs preliminary detection of moving objects and creation of abstracted images continuously during manual tracking mode. When the operator switches to automatic tracking mode, the tracking target is immediately specified from the pre-detected objects without requiring the operator to manually point or select, eliminating the time loss that would otherwise occur during mode switching.
Solution Approach 2:
The system provides self-service by automatically specifying the tracking target based on pre-detected moving objects when automatic tracking mode is activated. The computational processing device autonomously identifies the tracking target from the abstracted image without requiring additional manual input from the operator, thereby maintaining operability while eliminating time loss during mode transitions.
3Measurement precision
If detection accuracy depends on camera resolution, then measurement precision is improved, but the ability to detect from afar deteriorates
Solution Approach 1:
The system transitions from relying solely on spatial resolution (two-dimensional image quality) to using temporal information and motion detection (adding a time dimension). By detecting moving objects across multiple frames and creating abstracted images that highlight motion patterns, the system can accurately identify tracking targets even when they are distant, overcoming the limitation of fixed camera resolution.
Data Source
Figure 1
Figure 2~3
Figure 4~5
AI summary
A processing device includes circuitry configured to estimate a tracking target captured in image data by an imaging unit operating in a manual tracking mode, the manual tracking mode being a mode in which the tracking target is tracked by controlling at least one of a pan operation, a tilt operation and a zoom operation of the imaging unit by instructions from an external source.