Cross-Camera Object Labeling via Intermediary Server
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing surveillance camera systems face challenges in accurately tracking objects across multiple cameras due to inconsistent tracking labels, leading to lower accuracy in identifying and measuring the presence of individuals, especially when objects enter and leave the field of view, causing recognition errors and incorrect determination of suspicious persons.
Innovation Solution
An image processing apparatus that acquires and relates tracking results from multiple cameras to generate a unique object label, ensuring consistent identification across video images by using a label replacement dictionary to replace tracking labels with object labels, thereby maintaining accurate tracking and recognition of objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If tracking is performed independently in each camera's field of view, then each camera can track objects within its own view, but the same object is assigned different tracking labels across different cameras leading to identification errors
Solution Approach 1:
The patent introduces a label management server as an intermediary between multiple cameras and the tracking system. This server maintains a label replacement dictionary that maps local tracking labels to global object labels, enabling consistent identification across cameras while preserving independent tracking operations. The intermediary resolves the contradiction by coordinating label assignments without centralizing the tracking process itself.
Solution Approach 2:
The label replacement dictionary serves multiple functions: it acts as a translation layer between different camera coordinate systems, maintains object identity across camera boundaries, and enables both independent and coordinated tracking operations. This universal mechanism allows the system to simultaneously achieve ease of independent operation and reliability of cross-camera identification.
2Device complexity
If tracking labels are assigned independently by each camera, then the tracking process is simple and decentralized, but objects entering and leaving the field of view cause tracking interruptions and label inconsistencies
Solution Approach 1:
The system performs preliminary actions by pre-establishing the label replacement dictionary that maps potential tracking label combinations to unique object labels. This preparation allows the system to handle tracking interruptions and re-identifications efficiently without complex real-time calculations, maintaining both system simplicity and tracking precision.
Solution Approach 2:
The label management server implements feedback mechanisms by monitoring tracking label assignments from multiple cameras and adjusting the label replacement dictionary accordingly. When objects re-enter fields of view or new objects appear, the system uses feedback from tracking results to update label mappings, ensuring continuous accurate identification without increasing structural complexity.
3Area of stationary object
If multiple cameras cover a wide surveillance area, then the monitoring coverage is expanded, but the same object may be recognized as different objects by different cameras reducing overall tracking accuracy
Solution Approach 1:
The label management server acts as a mediator that reconciles observations from multiple cameras covering different areas. By maintaining a global label replacement dictionary that coordinates local tracking labels across all cameras, the system ensures that objects detected by different cameras receive consistent identification, thereby maintaining recognition consistency across expanded surveillance coverage.
Solution Approach 2:
The system merges tracking information from multiple cameras by combining their respective tracking results through the label replacement dictionary. This merging process integrates observations from different camera fields of view while maintaining object identity consistency, enabling reliable tracking across the entire expanded surveillance area rather than treating each camera independently.
Data Source
AI summary
An image processing apparatus includes an acquiring unit configured to acquire multiple tracking results about an object tracked in multiple video images captured by multiple imaging units. The tracking results correspond one-to-one to the video images. Each of the tracking results contains a position of the object detected from an image frame of the corresponding video image and a tracking label that identifies the object in the video image. The apparatus further includes a relating unit configured to relate objects, detected from image frames of the video images, based on the tracking results acquired by the acquiring unit to obtain relations and an object label generating unit configured to generate an object label based on the relations obtained by the relating unit. The object label uniquely identifies the object across the video images.