Abandoned Bicycle Identification via Multi-Source Image Analysis
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Solution Overview
Problem
Abandoned bicycles in cities occupy valuable space and pose safety risks due to their presence in sidewalks and other common areas, necessitating an effective method for identification and removal.
Innovation Solution
A computer-implemented system that processes visual media data from multiple sources to identify bicycles based on geolocation and timestamp data, determining abandonment status through factors like duration of presence and physical condition, and compiles a list for city officials to remove them.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual identification methods are used for abandoned bicycles, then the process is simple to implement, but the productivity is low and cannot handle large volumes of bicycles efficiently
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated computer vision system that uses image processing algorithms to detect and identify abandoned bicycles. The system automatically analyzes images from multiple sources, extracts features, and determines abandonment status without human intervention, thereby dramatically increasing identification throughput while managing complexity through software-based solutions.
Solution Approach 2:
The system creates digital copies of physical bicycles through image capture from multiple sources (street view images, user-submitted photos, etc.). These digital representations are then processed and analyzed to identify abandoned bicycles, eliminating the need for physical inspection and enabling parallel processing of multiple bicycles simultaneously, thus improving productivity.
2Productivity
If automated image processing is implemented to identify abandoned bicycles, then the productivity increases, but the measurement precision may be insufficient to accurately distinguish abandoned from non-abandoned bicycles
Solution Approach 1:
The patent segments the image analysis process into multiple independent stages: image acquisition from multiple sources, pre-processing to enhance quality, feature extraction (detecting bicycle components, position, orientation), abandonment factor analysis (duration, location, physical state), and final classification. This segmentation allows each stage to be optimized independently, maintaining high precision while enabling parallel processing for improved productivity.
Solution Approach 2:
The system implements feedback mechanisms where identification results are continuously refined. Multiple image sources provide redundant information that cross-validates findings, and the system uses confidence scoring to iteratively improve detection accuracy. Feedback from false positives/negatives can be used to retrain models, ensuring high precision is maintained as the system scales to handle large volumes of bicycles.
3Measurement precision
If multiple visual media sources are integrated to improve identification accuracy, then the measurement precision improves, but the device complexity increases due to data integration requirements
Solution Approach 1:
The patent designs a universal image processing platform that can handle multiple types of visual media sources (street view images, user-submitted photos, surveillance footage) through a common architecture. The system uses standardized image formats and processing pipelines that work across different source types, allowing multi-source integration to improve accuracy without proportionally increasing complexity. The same feature extraction and analysis algorithms are applied regardless of the image source.
Data Source
AI summary
Embodiments include method, systems and computer program products for identifying abandoned objects. In some embodiments, first visual media data of an object can be received from a first source. The first visual media data of the object can be processed to identify a type of the object. An identifier associated with the type of the object can be generated. The object can be identified in second visual media data received from a second source. A status of the object can be determined based at least in part on at least one abandonment factor derived from the first visual media data and the second visual media data. The identifier associated with the object and a location associated with the object can be added to a list of objects having the same status, based at least in part on the status of the object.