Video Frame Selection Using Multi-Rate Sampling for Real-Time Action Detection
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Solution Overview
Problem
Existing video frame selection methods are inefficient due to the high computational burden of machine learning algorithms, particularly deep neural networks, which struggle to process large video sequences in real-time, making it challenging to select relevant frames within a reasonable time frame, especially on resource-constrained devices like smartphones and cameras.
Innovation Solution
A method that involves sampling video frames at different rates, using a first sampling rate for detecting predetermined actions and a higher second sampling rate for capturing short duration actions, allowing for efficient frame selection while adhering to a processing budget, utilizing convolutional neural networks with varying accuracy and resolution to optimize processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms (deep neural networks) are used to detect desirable actions in video frames, then detection accuracy is improved, but processing time increases significantly
Solution Approach 1:
The video processing is divided into multiple passes: a first pass processes every Nth frame at full resolution using deep neural networks for accurate action detection, while a second pass processes intermediate frames at lower resolution. This segmentation allows the system to maintain high detection accuracy for key frames while reducing overall processing time by avoiding full-resolution analysis of all frames.
Solution Approach 2:
The system applies full computational resources (deep neural networks at full resolution) only partially - specifically to selected key frames where action detection is most critical. For other frames, a simplified processing approach is used, which is excessive for those particular frames but necessary to maintain real-time performance. This partial application of full-strength processing resolves the contradiction between accuracy and speed.
2Measurement precision
If deep neural networks are applied to all video frames, then action detection accuracy is maintained, but computational load becomes unmanageable for real-time processing
Solution Approach 1:
The frame processing is segmented into different groups: key frames processed with full deep neural network analysis, and intermediate frames processed with reduced computation. This segmentation enables the system to maintain high frame selection accuracy for important moments while achieving manageable processing throughput by reducing computational intensity for less critical frames.
Solution Approach 2:
Different processing qualities are applied to different portions of the video stream. Key frames (such as those containing detected actions or significant events) receive high-quality deep neural network processing, while intermediate frames receive lower-quality processing. This local differentiation in processing quality allows the system to maintain overall frame selection accuracy while reducing total computational load to sustainable levels.
Data Source
AI summary
A system and method of selecting a frame capturing a short duration action from a video. The method including receiving a sequence of frames of the video; and determining a reference frame in the sequence of frames by detecting a predetermined action captured in one of a first plurality of frames from the sequence of video frames, the first plurality of frames being sampled from the sequence of frames at a first sampling rate lower than that of the sequence of frames. The method further comprises selecting a second plurality of frames from the sequence of frames, the second plurality of frames having a location associated with the reference frame in the sequence and being selected at a second sampling rate, the second sampling rate being higher than the first sampling rate; and selecting the frame capturing the short duration action from the second plurality of frames.