Multi-Frame CNN Object Detection via Temporal Feature Fusion
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Solution Overview
Problem
Existing object recognition systems, particularly convolutional neural networks (CNNs), face challenges in accurately detecting humans or objects in real-time due to issues like occlusion, varying poses, and lighting conditions, especially when relying solely on single-frame RGB images, which can lead to false positives and missed detections.
Innovation Solution
A system and method utilizing multi-frame image features with temporal information, where a convolutional neural network processes a time series of images to generate feature maps and region proposal networks, incorporating previous frames' data to improve detection accuracy and robustness, enabling real-time object recognition even with occlusions and pose variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single-frame RGB images are used for object detection, then the system is simpler and faster to process, but detection accuracy decreases due to occlusion, varying poses, and lighting conditions
Solution Approach 1:
The system performs preliminary action by capturing and storing multiple frames of image data before making a detection decision. Instead of relying on a single frame, the system accumulates temporal information from multiple consecutive frames, allowing it to overcome occlusion and pose variations by analyzing the object across time. This preliminary accumulation of data improves detection accuracy while the processing remains manageable through efficient temporal feature extraction.
2Reliability
If multi-frame image data with temporal information is used, then detection accuracy and robustness improve, but processing complexity and computational cost increase
Solution Approach 1:
The system applies the extraction principle by selectively extracting only the relevant temporal features from multi-frame image data. Instead of processing all raw pixel information from multiple frames, the system extracts key temporal characteristics and features that are most indicative of object presence and identity. This selective extraction maintains detection robustness while significantly reducing processing complexity compared to analyzing complete multi-frame datasets.
Solution Approach 2:
The system segments the detection process into distinct temporal stages, processing different frames at different stages of the detection pipeline. By dividing the temporal sequence into manageable segments and processing them through specialized network layers, the system handles multi-frame data more efficiently. This segmentation allows the system to maintain high reliability through comprehensive temporal analysis while reducing overall processing complexity through structured, modular computation.
3Measurement precision
If deep convolutional neural networks are used for single-image classification, then object recognition accuracy improves, but real-time processing capability decreases due to computational expense
Solution Approach 1:
The system implements continuity of useful action by processing image data in continuous temporal streams rather than as discrete, independent frames. The temporal convolutional networks and recurrent neural networks maintain continuous state information across frames, allowing the system to accumulate useful computational work over time. This continuous processing approach enables real-time performance by keeping the computational pipeline continuously engaged and efficient, rather than restarting processing for each frame.
Solution Approach 2:
The system transitions from processing only spatial dimensions (2D images) to incorporating the temporal dimension (3D video data). By adding this fourth dimension to the feature extraction process through temporal convolutional networks and recurrent layers, the system achieves higher recognition accuracy through multi-dimensional feature analysis. This dimensional expansion allows the network to leverage temporal patterns and motions that are invisible in single frames, improving accuracy while maintaining real-time capability through efficient 4D convolution operations.
Data Source
AI summary
A method, a system, and a computer readable recording medium are disclosed for performing object recognition. The method includes receiving image data from an image; performing a multilayer feature extraction on the image data; generating current feature maps from the multilayer feature extraction; generating a region of proposal network map from at least the current feature maps, the region of proposal network map having one or more regions of interest; inputting previously generated feature maps and the current feature maps into a classifier with the region of proposed network map; and classifying the one or more regions of interest in the region of proposal network map.