Surveillance Path Analysis for Suspicious Activity Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current surveillance systems struggle to detect and analyze suspicious activities and associate them with potential regions of interest effectively, particularly in public spaces where perpetrators may not appear together, making it difficult to identify co-appearances and durations of interest for post-attack or crime investigation.
Innovation Solution
A method and apparatus that determine a duration and region of interest by analyzing image sequences from surveillance cameras, identifying characteristic information, and constructing paths for target and related subjects to find minimum distances and timestamps, thereby identifying potential regions of interest for co-appearances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If surveillance systems monitor all public spaces continuously, then detection coverage is improved, but data processing complexity and time consumption increase significantly
Solution Approach 1:
The patent segments the surveillance data processing by dividing public spaces into multiple regions of interest and processing video feeds from different cameras and time periods separately. This allows parallel processing of multiple data streams, reducing overall time consumption while maintaining comprehensive detection coverage across all monitored areas.
Solution Approach 2:
The system performs preliminary actions by pre-processing video data to extract key features such as object detection, tracking, and behavior pattern recognition before full analysis. This preliminary processing filters out normal activities and prepares data structures that accelerate subsequent suspicious activity detection, reducing the time required for comprehensive monitoring.
2Measurement precision
If surveillance systems analyze detailed behavior patterns, then detection precision is improved, but computational complexity increases
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific regions of interest where suspicious activities are detected rather than uniformly analyzing all areas. The system adjusts analysis depth based on local conditions, applying detailed behavior pattern recognition only to areas flagged as potentially suspicious, thereby maintaining high detection precision while reducing overall computational complexity.
Solution Approach 2:
The system performs partial analysis by initially applying simplified detection algorithms to filter obvious suspicious activities, then applying more complex behavior pattern analysis only to cases that pass the initial filter. This two-stage approach achieves high detection precision for critical cases while avoiding the computational burden of applying full analysis to all data.
3Productivity
If surveillance data is processed and categorized meaningfully, then investigation efficiency is improved, but data processing time increases
Solution Approach 1:
The patent implements preliminary categorization and organization of surveillance data by creating structured data formats that pre-classify activities by type, location, and time. This preliminary structuring allows investigators to quickly access and filter relevant information without requiring comprehensive re-processing of raw data, thereby improving investigation efficiency while minimizing additional processing time.
Solution Approach 2:
The system creates simplified copies or representations of complex surveillance data in structured formats that are easier to process and analyze. These data copies contain essential information in organized forms that accelerate investigation workflows without requiring investigators to work with the full complexity of原始 surveillance data, thus improving productivity with minimal time overhead.
Data Source
AI summary
The method comprising determining a set of coordinates each for two or more appearances of a target subject within a sequence of images, the set of coordinates of the two or more appearances of the target subject defining a first path; determining a set of coordinates each for two or more appearances of a related subject within a sequence of images, the related subject relating to the target subject, the set of coordinates of the two or more appearances of the related subject defining a second path; determining one or more minimum distances between the first path and the second path so as to determine at least a region of interest; determining a timestamp of a first appearance and a timestamp of a last appearance of the target subject; and determining a timestamp of a first appearance and a timestamp of a last appearance of the related subject.