Depth-Aware Object Counting via Multicolumn CNN Segmentation
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Solution Overview
Problem
Current machine learning technologies face challenges in accurately counting objects in images, particularly due to perspective-induced size variations, which affect the accuracy of density maps and object counting in crowded scenes or scenes with varying object sizes.
Innovation Solution
A method using a multicolumn convolutional neural network (MCCNN) that segments images based on depth information to apply filters of varying sizes to different regions, allowing for more accurate density map generation and object counting by accounting for perspective effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single filter size is used for the entire image, then the device complexity is reduced, but the measurement precision of object density deteriorates due to perspective-induced size variations
Solution Approach 1:
The image is segmented into multiple regions based on depth information, where each region corresponds to objects at similar distances from the camera. This segmentation allows different filter sizes to be applied to different regions, resolving the perspective-induced size variations and improving density estimation accuracy without requiring a single complex filter to handle all scales.
Solution Approach 2:
Different filter sizes are assigned to different spatial regions of the image based on the depth information. Regions closer to the camera use smaller filters while distant regions use larger filters, creating a locally optimized filtering strategy that adapts to the perspective variations in different parts of the image.
2Measurement precision
If depth information processing is added to select filters, then the measurement precision of object counting improves, but the processing time increases
Solution Approach 1:
Depth information is processed in advance to pre-segment the image into regions before applying the filtering operations. This preliminary segmentation based on depth maps allows the subsequent filtering and density estimation to proceed efficiently without requiring complex real-time decisions about filter selection during the main processing pipeline.
3Adaptability or versatility
If multiple convolutional networks with different filter sizes are used, then the adaptability to different object sizes improves, but the device complexity increases
Solution Approach 1:
The multicolumn convolutional network is segmented into multiple independent columns, where each column contains convolutional layers with filters of a specific size. This segmentation allows each column to specialize in detecting objects of particular sizes, and the depth-based region segmentation determines which column processes each region, providing adaptability without requiring a single complex network to handle all scales.
Solution Approach 2:
The network dynamically selects which column (and thus which filter size) to apply to each region based on the depth information. This dynamic adaptation allows the system to adjust its processing strategy to match the actual scale of objects in different regions, improving versatility while maintaining a modular structure that manages complexity.
Data Source
AI summary
Methods and apparatus, including computer program products, are provided for depth-aware object counting. In some example embodiments, there may be provided a method that includes processing, by the trained machine learning model, a first segment of an image and a second segment of the image, the first segment being processed using a first filter selected, based on depth information, to enable formation of a first density map, and the second segment being processed using a second filter selected, based on the depth information, to enable formation of a second density map; combining, by the trained machine learning model, the first density map and the second density map to form a density map for the image; and providing, by the trained machine learning model, an output based on the density map. Related systems, methods, and articles of manufacture are also described.