Spatio-Temporal Feature Maps for Real-Time Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional object detection neural networks process video frames independently, lacking the ability to effectively exploit temporal cues and resulting in less accurate and unstable outputs, while also being resource-intensive, making them unsuitable for real-time processing in resource-constrained environments.
Innovation Solution
The implementation of an object detection neural network that incorporates a recurrent neural network layer, specifically a bottleneck recurrent neural network layer, which processes video frames sequentially to generate spatio-temporal feature maps, enabling the propagation of frame-level information across time and reducing computational resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional object detection neural networks process video frames independently, then device complexity is reduced, but measurement precision and stability of object detection outputs deteriorate
Solution Approach 1:
The recurrent neural network layer maintains continuous temporal processing by passing spatio-temporal feature maps across multiple video frames. This continuity allows the network to accumulate temporal information and maintain consistent object detection across frames, resolving the contradiction between simple independent processing and accurate temporal-aware detection.
Solution Approach 2:
The patent introduces a temporal dimension to the feature maps by generating spatio-temporal feature maps that combine spatial information from convolutional layers with temporal information from recurrent layers. This dimensional extension enables the network to capture temporal cues without significantly increasing overall system complexity.
2Productivity
If conventional object detection neural networks process video frames independently, then processing speed is improved, but stability of object detection outputs deteriorates
Solution Approach 1:
The recurrent neural network layer maintains continuous temporal processing by passing spatio-temporal feature maps across multiple video frames. This continuity allows the network to accumulate temporal information and maintain consistent object detection across frames, resolving the contradiction between simple independent processing and accurate temporal-aware detection.
3Measurement precision
If object detection neural networks incorporate recurrent neural network layers to process video frames sequentially, then measurement precision and stability improve, but device complexity and resource consumption increase
Solution Approach 1:
The network is segmented into distinct functional modules: convolutional layers for spatial feature extraction, recurrent layers for temporal feature extraction, and detection layers for final object identification. This modular segmentation allows each component to specialize in specific tasks, improving overall accuracy while managing complexity through clear functional separation.
Solution Approach 2:
The patent introduces a temporal dimension to the feature maps by generating spatio-temporal feature maps that combine spatial information from convolutional layers with temporal information from recurrent layers. This dimensional extension enables the network to capture temporal cues without significantly increasing overall system complexity.
4Measurement precision
If object detection neural networks incorporate recurrent neural network layers, then detection accuracy improves, but resource consumption increases making real-time processing difficult
Solution Approach 1:
The patent extracts only the essential temporal features needed for object detection by using recurrent neural network layers to process spatio-temporal feature maps. This selective extraction of temporal information maintains detection accuracy while reducing unnecessary computational overhead compared to processing all frame data in full detail.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing object detection. In one aspect, a method includes receiving multiple video frames. The video frames are sequentially processed using an object detection neural network to generate an object detection output for each video frame. The object detection neural network includes a convolutional neural network layer and a recurrent neural network layer. For each video frame after an initial video frame, processing the video frame using the object detection neural network includes generating a spatial feature map for the video frame using the convolutional neural network layer and generating a spatio-temporal feature map for the video frame using the recurrent neural network layer.