ANN Traffic Density Estimation Using FCN and LSTM
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to accurately estimate traffic density and flow from low-quality, low-resolution traffic camera images due to varying weather conditions, lighting, and diverse vehicle types, making it challenging to integrate this data into intelligent traffic systems effectively.
Innovation Solution
The use of artificial neural networks (ANNs), specifically trained on annotated and synthetic images, to estimate traffic density and flow by processing low-resolution images from traffic cameras, combined with fully convolutional networks (FCNs) and long short-term memory (LSTM) networks to handle temporal data and adapt to different camera perspectives and conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing traffic analysis methods are used on low-quality camera images, then processing speed is maintained, but measurement precision of traffic density and flow deteriorates
Solution Approach 1:
The patent transforms the input image parameters by converting low-quality images into enhanced representations through preprocessing techniques including noise reduction, contrast adjustment, and resolution enhancement. This allows the system to maintain high measurement precision while working with low-quality camera images that would otherwise be unsuitable for accurate traffic analysis
Solution Approach 2:
The patent introduces an intermediary deep learning model that acts as a bridge between low-quality input images and the traffic density/flow estimation process. This intermediary model performs intermediate processing to extract meaningful features from degraded images, enabling accurate measurements without requiring high-quality input images
2Adaptability or versatility
If deep learning models are trained on diverse traffic conditions, then adaptability to different weather and lighting conditions improves, but device complexity increases
Solution Approach 1:
The patent performs preliminary action by pre-training deep learning models on extensive datasets covering diverse weather conditions, lighting scenarios, and traffic patterns before deployment. This preliminary training enables the model to adapt to various conditions without requiring complex real-time adjustments, reducing operational complexity while maintaining high adaptability
Solution Approach 2:
The patent creates a universal deep learning model that handles multiple functions including density estimation, flow measurement, and adaptation to various environmental conditions within a single system. This multi-functional approach reduces overall device complexity compared to having separate specialized systems for each condition
3Measurement precision
If low-resolution images are used from traffic cameras, then data transmission and storage requirements are reduced, but measurement precision of traffic parameters deteriorates
Solution Approach 1:
The patent replaces the mechanical approach of capturing high-resolution images with a computational approach using deep learning models. The system substitutes physical image quality improvements with intelligent algorithms that can extract accurate traffic parameters from low-resolution images, maintaining measurement precision without requiring high image resolution
Data Source
AI summary
Methods and software utilizing artificial neural networks (ANNs) to estimate density and/or flow (speed) of objects in one or more scenes each captured in one or more images. In some embodiments, the ANNs and their training configured to provide reliable estimates despite one or more challenges that include but are not limited to, low-resolution images, low framerate image acquisition, high rates of object occlusions, large camera perspective, widely varying lighting conditions, and widely varying weather conditions. In some embodiments, fully convolutional networks (FCNs) are used in the ANNs. In some embodiments, a long short-term memory network (LSTM) is used with an FCN. In such embodiments, the LSTM can be connected to the FCN in a residual learning manner or in a direct connected manner. Also disclosed are methods of generating training images for training an ANN-based estimating algorithm that make training of the estimating algorithm less costly.