Temporal-Spatial Pyramid Pooling Layer for Variable-Length Video Action Detection
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Solution Overview
Problem
Conventional video action detection methods using 3D CNNs face limitations due to the requirement of consistent video clip sizes, leading to issues like sparse sampling that disrupt action continuity and increase intra-class differences, requiring more complex networks and training data.
Innovation Solution
The integration of a temporal-spatial pyramid pooling layer between the convolutional and full connection layers in a CNN allows for uniform feature extraction across varying input sizes, eliminating the need for consistent video clip lengths and enhancing feature robustness and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video clips are downsampled to achieve consistent input sizes for 3D CNN, then the network can process variable-length videos, but the continuity within actions is destroyed and action features cannot be learned effectively
Solution Approach 1:
The patent introduces a temporal pyramid pooling layer that processes video features across multiple temporal dimensions simultaneously. Instead of downsampling videos to a fixed length, the method divides the temporal dimension into multiple scales (e.g., 1/1, 1/2, 1/4, 1/8 of original length) and extracts features at each scale, then fuses them. This dimensional transformation allows the network to accept variable-length videos while preserving action continuity at all temporal scales.
2Reliability
If multiple networks with different structures are used to process segments at different sampling frequencies, then comprehensive features can be extracted, but the device complexity and training time increase significantly
Solution Approach 1:
The patent merges multiple processing paths into a single unified network. Instead of using separate networks for different sampling frequencies, the method uses one 3D CNN to extract features from the entire video, then passes these features through a temporal pyramid pooling layer that handles multiple temporal scales within the same network. This consolidation reduces device complexity and training time while maintaining comprehensive feature extraction capabilities.
Solution Approach 2:
The temporal pyramid pooling layer serves multiple functions within a single component: it processes features at different temporal scales, fuses multi-scale features, and adapts to variable input lengths. This multi-functional design eliminates the need for multiple specialized networks, reducing overall system complexity while achieving comprehensive feature extraction.
3Productivity
If uniform downsampling is applied to all video clips, then consistent input sizes are achieved, but intra-class differences increase and more complex networks are required
Solution Approach 1:
The temporal pyramid pooling layer segments the temporal dimension into multiple scales (1/1, 1/2, 1/4, 1/8), processing features at each scale independently and then fusing them. This segmentation allows the network to capture actions at different temporal resolutions without requiring uniform downsampling of the entire video, thereby maintaining training efficiency while reducing the need for overly complex networks.
Data Source
AI summary
A video action detection method based on a convolutional neural network (CNN) is disclosed in the field of computer vision recognition technologies. A temporal-spatial pyramid pooling layer is added to a network structure, which eliminates limitations on input by a network, speeds up training and detection, and improves performance of video action classification and time location. The disclosed convolutional neural network includes a convolutional layer, a common pooling layer, a temporal-spatial pyramid pooling layer and a full connection layer. The outputs of the convolutional neural network include a category classification output layer and a time localization calculation result output layer. The disclosed method does not require down-sampling to obtain video clips of different durations, but instead utilizes direct input of the whole video at once, improving efficiency. Moreover, the network is trained by using video clips of the same frequency without increasing differences within a category, thus reducing the learning burden of the network, achieving faster model convergence and better detection.