Budget-Aware Video Activity Detection Using Reinforcement Learning
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Solution Overview
Problem
Existing activity detection processes in video analytics are computationally expensive and inefficient, particularly when trying to achieve high accuracy within a given time or computation budget, as they often rely on exhaustive feature evaluation and divide the detection process into disjointed proposal and classification steps.
Innovation Solution
A budget-aware method using a reinforcement learning algorithm and recurrent neural networks to sequentially select a subset of video frames for processing, optimizing the detection process according to a specified time or computation budget, while predicting temporal locations and classifications of activity segments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive feature evaluation and classification at every time step is used, then activity detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The video is divided into temporal segments or clips rather than processing every frame individually. The system identifies candidate segments that are likely to contain activities and focuses computational resources on these segments, reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
Instead of performing exhaustive classification at every time step, the system performs partial classification only on selected temporal segments. This partial action approach reduces computational load significantly while still achieving competitive detection accuracy by focusing resources on the most relevant portions of the video.
2Measurement precision
If a large set of features and classifiers is used, then activity detection accuracy is improved, but computational resources increase
Solution Approach 1:
The system extracts and processes only the necessary features and classifiers for the specific temporal segments being analyzed, rather than applying a large set of features and classifiers to every frame. This extraction approach reduces computational resource consumption while maintaining detection accuracy.
Solution Approach 2:
The system applies classification operations partially - only on selected temporal segments rather than exhaustively on all video content. This reduces the total computational resources required while maintaining competitive detection accuracy through focused analysis of relevant segments.
3Productivity
If temporal segments are proposed and classified in separate steps, then processing efficiency is improved, but detection accuracy deteriorates due to disjointed steps
Solution Approach 1:
The system merges the proposal and classification steps into a unified recurrent neural network framework. The RNN simultaneously performs temporal segment selection and activity classification in an integrated manner, eliminating the disjointed nature of separate steps while maintaining processing efficiency.
Solution Approach 2:
The system uses a dynamic recurrent neural network that adapts its processing based on the video content. The RNN can adjust which temporal segments to focus on and how to classify them based on learned patterns, providing both efficiency and accuracy through dynamic decision-making rather than rigid separate steps.
Data Source
AI summary
Detection of activity in video content, and more particularly detecting in video start and end frames inclusive of an activity and a classification for the activity, is fundamental for video analytics including categorizing, searching, indexing, segmentation, and retrieval of videos. Existing activity detection processes rely on a large set of features and classifiers that exhaustively run over every time step of a video at multiple temporal scales, or as a small improvement computationally propose segments of the video on which to perform classification. These existing activity detection processes, however, are computationally expensive, particularly when trying to achieve activity detection accuracy, and moreover are not configurable for any particular time or computation budget. The present disclosure provides a time and/or computation budget-aware method for detecting activity in video that relies on a recurrent neural network implementing a learned policy.