Cross-Channel Feature Extraction for Object Detection Accuracy
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Solution Overview
Problem
Current object detection methods in computer vision face limitations in feature extraction performance, which affects the accuracy and efficiency of object detection in images and videos, particularly in applications like intelligent video surveillance and vehicle navigation.
Innovation Solution
The method involves generating multiple image channels through non-linear conversion, extracting intra-channel features from individual channels, and cross-channel features from multiple channels, which are then used for feature selection and classifier training to enhance object detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional hand-crafted features (HC) are used, then the feature extraction process is simple and computationally efficient, but the discrimination capability and detection accuracy are insufficient
Solution Approach 1:
The patent merges hand-crafted feature extraction with deep learning by combining traditional HCLA methods with cross-channel feature extraction. The system integrates multiple image channels (RGB plus additional channels) and extracts features both within individual channels and across channels, creating a hybrid approach that maintains computational efficiency while significantly improving discrimination capability and detection accuracy.
Solution Approach 2:
The patent adds new dimensions to feature extraction by introducing cross-channel features alongside traditional intra-channel features. Instead of only extracting features from single channels, the system extracts features that span multiple channels simultaneously, creating a multi-dimensional feature space that enhances discrimination capability without proportionally increasing computational complexity.
2Measurement precision
If deep learning based features are used, then the discrimination capability is improved, but the computational complexity increases and high-performance hardware is required
Solution Approach 1:
The patent segments the feature extraction process into distinct components: intra-channel feature extraction and cross-channel feature extraction. This segmentation allows the system to process different types of features through separate mechanisms, maintaining computational efficiency while improving discrimination capability. The cross-channel features are extracted by combining information from multiple channels in a structured manner that avoids the full computational burden of traditional deep learning.
3Quantity of substance
If the number of image channels is increased, then the feature richness and discrimination capability are improved, but the computational burden and processing time increase
Solution Approach 1:
The patent applies partial action by extracting features selectively from multiple channels rather than processing all possible channel combinations exhaustively. The cross-channel feature extraction mechanism processes patches from different channels in a structured manner that provides sufficient feature richness without requiring complete enumeration of all channel combinations, thus maintaining processing speed.
Data Source
AI summary
Methods, systems and apparatuses of feature extraction and object detection are provided. In the method of feature extraction, a plurality of image channels are generated from each of training images; intra-channel features are extracted from the plurality of image channels for each of training images, wherein the intra-channel features include features independently extracted from a single image channel; cross-channel features are extracted from the plurality of image channels for at least one of the training images, wherein the cross-channel features include features extracted from at least two image channels. The intra-channel features and the cross-channel features form a set of features for feature selection and classifier training. With the above method, cross-channel features, which reflect discriminant information across different image channels, can be further used for object detection together with the intra-channel features, and thus there are much richer features for object detection and better accuracy of object detection can be achieved.