Video Image Interpretation for Moving Objects
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Solution Overview
Problem
Existing systems for recording and analyzing video images at remote locations lack comprehensive features for temporal and spatial data interpretation, object and event search, and compact presentation, limiting their effectiveness in applications like the railroad industry and other environments with moving objects.
Innovation Solution
A method and apparatus that integrates temporal data interpretation, allows for geographic querying, and incorporates optical character recognition, using cameras with infrared and color capabilities to capture and store images, along with optical flow measurement for speed calculation, and software for processing pixel flow information to create linear panoramas, enabling efficient search and analysis of moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If comprehensive temporal and spatial data interpretation features are integrated into the system, then the ability to search and analyze objects and events is improved, but the device complexity increases
Solution Approach 1:
The system integrates multiple functions into a unified platform that handles video capture, temporal analysis, spatial querying, optical character recognition, and panorama generation. The data processing system serves as a universal hub that coordinates cameras, databases, and various analysis algorithms, allowing a single system to perform diverse security and forensic tasks across different locations and time periods.
Solution Approach 2:
The system employs a hierarchical structure where the central data processing system contains and coordinates multiple specialized components. Within the database layer, temporal data and spatial data are nested in separate but interconnected structures. The spatial database contains geographic information that can be queried independently, while the temporal database stores time-stamped video data. This nested organization allows complex queries to be broken down into manageable sub-queries.
2Reliability
If multiple camera systems with infrared and color capabilities are deployed at remote locations, then the quality and scope of image capture is improved, but the loss of time for data transmission and processing increases
Solution Approach 1:
The system performs preliminary processing of video data at the remote locations before transmission to the central facility. Individual frames are captured and pre-processed by local systems, with only relevant data (such as detected objects, temporal anomalies, or query-matched frames) being transmitted to the central database. This reduces the volume of data that needs to be transmitted and processed centrally, thereby minimizing time loss.
Solution Approach 2:
The video data stream is segmented into individual frames that are independently analyzed and stored. The temporal database stores these segmented frames with precise time stamps, allowing the system to retrieve only specific time periods rather than transmitting or processing entire video sequences. This segmentation enables efficient random access to temporal data without the overhead of processing continuous video streams.
3Measurement precision
If optical flow measurement and pixel flow analysis are implemented for speed calculation, then the measurement precision of moving objects is improved, but the use of energy for processing increases
Solution Approach 1:
The system applies optical flow analysis selectively rather than continuously. Optical character recognition and pixel flow analysis are triggered only when specific conditions are met, such as when an object enters a region of interest, when motion is detected above a threshold, or when a spatial query is initiated. This partial application of complex processing algorithms reduces overall energy consumption while maintaining high measurement precision when needed.
Solution Approach 2:
The system introduces an intermediate layer of motion detection that filters video data before applying energy-intensive optical flow analysis. Simple motion detection algorithms first identify potential objects of interest, and only then are the more computationally demanding pixel flow and optical character recognition algorithms applied to those specific regions. This intermediary filtering step significantly reduces the total energy required for processing while preserving measurement precision for relevant objects.
Data Source
AI summary
This is a method and apparatus to create and interpret images in a temporal or spatial domain, which will be helpful with any moving objects. The images are captured and stored in a database. The video images can be viewed in a panoramic fashion or dissected into individual frames or pictures to make the searching of particular objects or events easier.