Object Appearance Frequency Estimation Using Vehicle Camera Data
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Solution Overview
Problem
Existing techniques for estimating object appearance frequency, such as pedestrians and bicycles, outside a vehicle on a predetermined driving path are limited as they primarily rely on vehicle location data and do not effectively utilize environmental data, making it difficult to apply traffic congestion prediction technologies to pedestrian estimation.
Innovation Solution
An object appearance frequency estimating apparatus mounted on a vehicle that collects and processes space-time information and frequency data using a camera module, recognition module, and calculating module to estimate object appearance frequencies in a predetermined area, incorporating reliability degrees and learning data to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traffic congestion prediction technology relying on vehicle location data is applied to pedestrian estimation, then the device complexity is reduced, but the measurement precision of object appearance frequency deteriorates
Solution Approach 1:
The system segments the estimation approach by separating vehicle-based probe data collection from actual pedestrian/bike recognition. Image recognition modules in vehicles capture and identify pedestrians and bicycles, converting visual data into frequency information that is then aggregated by the center server, thus adapting the segmentation principle to resolve the contradiction between simple device architecture and precise measurement.
Solution Approach 2:
The patent introduces an intermediary conversion process where image recognition modules act as mediators between raw visual environment data and structured frequency information. This intermediary layer transforms complex image data into quantifiable frequency metrics that can be processed by the estimation system, enabling precise pedestrian and bike frequency measurement without requiring direct complex detection infrastructure at every node.
2Ease of operation
If vehicle-based probe data collection is used, then the ease of operation is improved, but the reliability of object appearance frequency estimation deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the center server aggregates frequency information from multiple vehicle probes, calculates appearance frequencies based on accumulated data, and refines estimation accuracy over time. This feedback loop allows the system to improve reliability through continuous data accumulation and statistical processing while maintaining the ease of operation of vehicle-based data collection.
Solution Approach 2:
The patent changes the parameter of data representation from simple vehicle location coordinates to structured frequency information containing object counts, spatial distribution, and temporal patterns. This parameter transformation enables more reliable estimation by capturing the actual appearance frequency characteristics of pedestrians and bicycles, thereby improving reliability while preserving the operational simplicity of mobile data collection.
Data Source
AI summary
An estimation apparatus of an object appearance frequency is provided. The apparatus estimates an appearance frequency of objects, such as pedestrians, in a predetermined estimation area. The apparatus calculates a matrix FPFP by searching an appearance frequency data in the past. In detail, an estimated result that is an output of an estimating module is expressed as a vector. Objects to be estimated are classified into a total of 12 kinds, such as a “man, woman, child, bike, unknown, dog” and a “right, left”. Feature vector of appearance frequency of pedestrians, i.e., objective variables of estimation, is expressed by 12th dimension vector space. Moreover, status information is used as explaining variables, which explain the feature vectors when a feature vector occurs. The regression relationship of the feature vector with respect to the status information vector expressed in 28th dimension is solved by the linear least squares method.