Multi-camera Traffic Trend Analysis Using Landmark Mapping
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Solution Overview
Problem
Current surveillance systems face inefficiencies in monitoring and identifying events of interest from multiple video streams, requiring human intervention and often leading to missed or mislabeled events due to the need for individual monitoring of each stream.
Innovation Solution
A system that processes video streams from multiple sources to identify landmark points and traffic areas, using these to monitor and analyze traffic trends, presenting trends on an overhead map for efficient event detection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple video streams are monitored individually by human operators, then event detection capability is maintained, but monitoring efficiency decreases and human error increases
Solution Approach 1:
The system enables self-service automation where the computer automatically performs video stream monitoring, event detection, and trend identification without human intervention. The automated computer vision system analyzes multiple video streams simultaneously, identifying traffic trends and generating heatmaps autonomously, thereby eliminating human error while maintaining high monitoring efficiency
Solution Approach 2:
The patent replaces the mechanical human monitoring process with an automated computer vision system. The system uses algorithms to process video streams, detect objects, track movement patterns, and generate traffic trend analyses automatically, substituting human operators with computational processes that achieve both high reliability and productivity
2Productivity
If automated computer vision is used to process video streams, then monitoring efficiency increases, but system complexity increases
Solution Approach 1:
The system segments the complex video analysis task into distinct functional modules: video stream reception, landmark point identification, traffic area definition, object detection, movement tracking, and heatmap generation. This modular segmentation manages system complexity by organizing functions into separate processing stages while maintaining high monitoring efficiency through automated computation
Solution Approach 2:
The patent implements a universal computer vision system that performs multiple functions simultaneously: detecting various types of objects (people, vehicles, animals), tracking their movements, identifying traffic patterns, generating heatmaps, and providing alerts. This multi-functional approach increases monitoring efficiency while managing complexity through integrated processing
3Measurement precision
If landmark points are identified to map video streams to overhead maps, then traffic trend analysis accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-identifying and storing landmark points in the physical environment before full traffic analysis begins. These landmark points (such as building corners, street intersections, or distinctive features) are detected and mapped to the overhead map in advance, creating a reference framework that enables faster and more accurate real-time traffic trend analysis without repeated processing
Solution Approach 2:
The patent creates a simplified copy or representation of the physical environment through the overhead map with embedded landmark points. This digital twin or abstract model allows the system to perform rapid spatial analysis and traffic pattern recognition without processing full-resolution video data continuously, thereby improving analysis accuracy while reducing processing time
Data Source
AI summary
Described herein are systems, methods, and software to monitor traffic trends associated with objects in a physical area using multiple video sources. In one implementation, a method includes receiving the video from the plurality of video sources and identifying landmark points of the physical area in the video. The landmark points correspond to objects represented in a map. The method also includes monitoring traffic in the physical area from the video and determining a trend in the traffic relative to the landmark points. The method further includes presenting the trend on the map relative to the objects.