Parked Vehicle Detector Training via Automated Sample Collection
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Solution Overview
Problem
Current parking management systems face challenges in efficiently detecting parked vehicles with high accuracy across varying conditions, as existing methods require costly retraining and are not suitable for real-time applications due to slow computation and poor performance.
Innovation Solution
An automated system for training parked vehicle detectors using video data, incorporating voluntary crowd-sourcing and vehicle re-identification units to collect positive and negative samples, which allows for efficient localization and retraining of vehicle detectors across different conditions, reducing the need for manual labor and improving real-time accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple vehicle detectors are used for different conditions, then detection accuracy is improved, but retraining cost and time increase
Solution Approach 1:
The patent creates a universal training framework that can handle multiple detection conditions (different poses, lighting, environments) through a single detector. The system collects diverse training samples across all conditions and trains one detector to handle all scenarios, eliminating the need for separate detectors for each condition while maintaining high accuracy.
Solution Approach 2:
The patent merges multiple training datasets covering different conditions (daytime, nighttime, various poses, different locations) into a unified training corpus. By combining these diverse samples and training a single detector on all of them simultaneously, the system achieves multi-condition detection capability without needing multiple separate detectors, thus reducing retraining time and resource consumption.
2Manufacturing precision
If manual identification and cropping of parked vehicles is performed, then training sample quality is improved, but labor cost and time increase
Solution Approach 1:
The patent implements automated systems that perform vehicle identification, localization, and sample cropping without human intervention. The system uses detection algorithms to automatically identify parked vehicles in video frames, localize them with bounding boxes, extract the relevant image regions, and organize training samples autonomously, eliminating the need for manual labor while maintaining sample quality.
Solution Approach 2:
The patent replaces manual mechanical operations (hand-cropping, visual inspection, sample collection) with automated computational methods. Detection algorithms, image processing techniques, and automated data pipelines substitute human operators, enabling high-volume sample collection with consistent quality and no additional labor costs.
3Ease of manufacture
If conventional image segmentation techniques are used, then image processing is performed, but computation speed is slow and real-time performance is poor
Solution Approach 1:
The patent extracts and uses only the essential features needed for vehicle detection from video frames, avoiding comprehensive image segmentation. Instead of dividing images into many small pieces based on color and texture, the system directly identifies and localizes vehicles using detection algorithms, extracting only the relevant information (vehicle presence, location, bounding box) required for training and real-time detection.
4Ease of manufacture
If conventional image segmentation techniques are used for training sample collection, then image processing is performed, but detection accuracy is poor
Solution Approach 1:
The patent replaces conventional image segmentation techniques with modern object detection algorithms for training sample collection. Instead of segmenting images based on low-level features like color and texture, the system uses learned detection models to accurately identify and localize vehicles, extracting training samples with precise bounding boxes and higher semantic accuracy, thereby improving detection performance.
Data Source
AI summary
Methods and systems for training a parked vehicle detector. Video data regarding one or more parking sites can be captured. Positive training samples can then be collected from the video data based on a combination of one or more automated computing methods and human-input auxiliary information. Additionally, negative training samples can be collected from the video data based on automated image analyses with respect to the captured video data. The positive training samples and the negative training samples can then be used to train, re-train or update one or more parked vehicle detectors with respect to the parking site(s) for use in managing parking at the parking site(s).