Object Trail Analytics Using Road Speed Forecasting
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Solution Overview
Problem
Analyzing object trails in video data relies heavily on manual analysis, which is inefficient and uneconomical, especially in large-scale urban and suburban areas where hundreds of hours of video need to be manually combed through.
Innovation Solution
A system that automatically performs object trail analysis by creating time series data for roads within a traffic network, extracting features, and using time series forecasting models to predict the next location and timing of a moving object, leveraging adjacency relations and routing checks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual video analysis is used to track moving objects, then analysis can be performed with simple tools, but the analysis time and cost increase significantly
Solution Approach 1:
The patent replaces manual mechanical video analysis with an automated computer vision system that uses machine learning models to detect, track, and analyze moving objects. The system automatically processes video feeds, extracts object trajectories, and generates analytics reports without human intervention, thereby dramatically improving analysis speed and reducing time loss.
Solution Approach 2:
The system enables self-service object trail analysis by automatically performing detection, tracking, and analysis functions that previously required manual human effort. The automated pipeline processes video data, identifies objects, tracks their movement across frames, and generates insights without requiring analysts to manually review hundreds of hours of footage.
2Productivity
If automated object detection is implemented, then analysis speed improves, but system complexity increases
Solution Approach 1:
The automated system is divided into distinct modular components: video feed ingestion module, object detection module using machine learning models, tracking module for trajectory analysis, and analytics generation module. Each component handles a specific task independently, making the overall complex system manageable through functional segmentation and allowing parallel processing.
3Measurement precision
If comprehensive video monitoring is performed, then object tracking accuracy improves, but computational resources and time consumption increase
Solution Approach 1:
The system performs preliminary object detection and classification in early processing stages to identify only relevant objects worth tracking. By pre-filtering video data and focusing computational resources on detected objects of interest rather than analyzing every pixel in every frame, the system maintains high tracking accuracy while reducing overall energy consumption.
Data Source
AI summary
A method, a structure, and a computer system for object trail analytics. The exemplary embodiments may include obtaining time series data detailing an average speed of one or more roads within a traffic network at one or more times. The exemplary embodiments may further include extracting one or more features corresponding to the time series data, and generating one or more time series forecasting models based on the time series data and the one or more features. Additionally, the exemplary embodiments may include identifying a current location of a moving object within the traffic network, and predicting a speed of the moving object based on applying the one or more time series forecasting models to the current location.