Deformable Convolution Receptive Fields for Action Detection
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Solution Overview
Problem
Traditional approaches to fine-grained action detection decouple spatial and temporal information, making it difficult to accurately and efficiently detect subtle motions in video data, especially when objects move with minimal changes across frames.
Innovation Solution
The system employs a neural network model with deformable convolution layers to extract motion vectors from adaptive receptive fields, concatenating these with spatial features to generate spatio-temporal features for autonomous action detection, eliminating the need for additional processing like optical flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If traditional approaches decouple spatial and temporal information for action detection, then the processing complexity is reduced, but the detection accuracy of subtle motions deteriorates
Solution Approach 1:
The patent merges spatial and temporal information processing within the deformable convolution layer by allowing convolution kernels to adaptively adjust their receptive fields based on motion patterns. This integration enables the network to simultaneously process spatial features and temporal motion information, resolving the contradiction between processing complexity and detection accuracy for subtle motions.
Solution Approach 2:
The patent introduces dynamic adaptive receptive fields that can automatically adjust their size and shape according to the detected motion magnitude. For regions with subtle motions, the receptive fields expand to capture more contextual information, while for regions with obvious motions, they maintain smaller sizes. This dynamic adaptation improves detection accuracy without uniformly increasing processing complexity across the entire image.
2Productivity
If traditional methods use fixed receptive fields for motion detection, then the computational efficiency is improved, but the ability to detect motions at different scales deteriorates
Solution Approach 1:
The patent replaces fixed receptive fields with dynamic adaptive receptive fields that automatically adjust their dimensions based on the local motion characteristics. This allows the system to efficiently process images while adapting to different motion scales, as the receptive field size is dynamically determined by the content rather than being uniformly fixed, thereby resolving the contradiction between computational efficiency and multi-scale detection capability.
Solution Approach 2:
The patent applies different receptive field sizes to different regions of the image based on local motion requirements. Regions with subtle or multi-scale motions receive larger or adjustable receptive fields, while regions with simple motions use smaller fields. This local adaptation maintains computational efficiency by avoiding uniform large receptive fields across the entire image while still providing multi-scale detection capability where needed.
3Measurement precision
If additional processing like optical flow is used for motion detection, then the motion analysis accuracy is improved, but the system complexity and processing time increase
Solution Approach 1:
The patent merges motion detection functionality directly into the deformable convolution layer, eliminating the need for separate optical flow processing modules. The adaptive receptive fields inherently capture temporal motion information through their deformation, integrating spatial and temporal analysis in a single unified operation. This reduces system complexity while maintaining motion analysis accuracy.
Solution Approach 2:
The patent extracts and integrates the essential motion detection capability within the convolution layer itself, removing the need for external optical flow processing. By embedding motion sensitivity directly in the feature extraction stage through adaptive receptive fields, the system achieves accurate motion analysis without the added complexity of separate motion processing pipelines.
Data Source
AI summary
Techniques regarding action detection based on motion in receptive fields of a neural network model are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a motion component that can extract a motion vector from a plurality of adaptive receptive fields in a deformable convolution layer of a neural network model. The computer executable components can also comprise an action detection component that can generate a spatio-temporal feature by concatenating the motion vector with a spatial feature extracted from the deformable convolution layer.