Two-Stage Online Action Start Detection in Streaming Video
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Solution Overview
Problem
Traditional action start detection in videos is not capable of providing real-time detection with sufficient precision, especially in time-sensitive scenarios such as autonomous driving and surveillance, due to the offline nature of per-frame analysis.
Innovation Solution
A two-stage online detection system (ODAS) comprising a classification module and a localization module, which processes streaming videos to identify action starts and classes in real-time by employing a deep convolutional network and recurrent neural networks, respectively, and utilizes reinforcement learning to maximize long-term reward for accurate start detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline per-frame analysis is used for action start detection, then measurement precision is improved, but loss of time increases due to requiring future video information
Solution Approach 1:
The detection process is divided into two independent stages: action classification (identifying what action is occurring) and start detection (identifying when the action begins). This segmentation allows each stage to be optimized independently, with the classification stage processing frames sequentially without needing future information, while the start detection stage uses the classification results to identify action beginnings in real-time.
Solution Approach 2:
The action classification is performed as a preliminary step before start detection. By classifying actions in advance using only past and present frame information, the system prepares the necessary context for subsequent start detection without requiring future video information, enabling real-time operation while maintaining precision.
2Measurement precision
If offline analysis using future video information is used, then measurement precision is improved, but productivity decreases due to inability to provide real-time detection
Solution Approach 1:
The system segments the detection task into classification and start detection components that can operate sequentially on incoming frames without waiting for future information. This enables continuous real-time processing (high productivity) while maintaining accurate detection through the two-stage approach.
Solution Approach 2:
The system processes each video frame independently using only information available up to that point, making each detection decision self-contained and not dependent on future frames. This self-service approach enables real-time processing while maintaining detection accuracy through the accumulated context from previous frames.
3Device complexity
If a single-stage detection system is used, then device complexity is reduced, but measurement precision worsens due to inability to separately optimize classification and localization
Solution Approach 1:
The detection system is segmented into two specialized modules: a classification module for action recognition and a start detection module for identifying action beginnings. This segmentation allows each module to be optimized for its specific function, improving overall precision while maintaining manageable complexity through modular design.
Solution Approach 2:
The two-stage system creates a reusable framework where the classification module can serve multiple purposes: identifying current actions, providing context for start detection, and enabling real-time processing. This multi-functionality justifies the additional stage by providing broader system capabilities beyond simple start detection.
Data Source
AI summary
Embodiments described herein provide a two-stage online detection of action start system including a classification module and a localization module. The classification module generates a set of action scores corresponding to a first video frame from the video, based on the first video frame and video frames before the first video frames in the video. Each action score indicating a respective probability that the first video frame contains a respective action class. The localization module is coupled to the classification module for receiving the set of action scores from the classification module and generating an action-agnostic start probability that the first video frame contains an action start. A fusion component is coupled to the localization module and the localization module for generating, based on the set of action scores and the action-agnostic start probability, a set of action-specific start probabilities, each action-specific start probability corresponding to a start of an action belonging to the respective action class.