Video Event Detection Using Dynamic Sliding Windows
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Solution Overview
Problem
Deep learning-based multimodal representation learning for natural language-based video event detection is computationally expensive, making it challenging to deploy on resource-constrained edge devices for real-time video analytics.
Innovation Solution
A method for real-time video event detection that generates a query vector from natural language queries and performs multimodal feature extraction using overlapping windows of sequential frames, comparing the similarity score to a threshold to activate an action trigger, optimized for resource-constrained edge devices by dynamically selecting feature extractors and using a dynamic sliding window technique.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning-based multimodal representation learning is used for video event detection, then detection accuracy is improved, but computational cost increases making it unsuitable for edge devices
Solution Approach 1:
The patent segments the video stream into overlapping temporal windows for processing. Each window is independently processed to generate event probabilities, allowing the system to handle long videos efficiently while maintaining detection accuracy. This segmentation reduces the computational burden compared to processing the entire video stream at once with deep learning models.
Solution Approach 2:
The patent implements dynamic window sizing where the window size adapts based on the detected event type and temporal characteristics. The system can adjust window sizes dynamically to optimize between detection accuracy and computational efficiency, allowing smaller windows for simple events and larger windows for complex temporal patterns, thereby reducing overall computational cost.
2Reliability
If real-time video event detection is implemented on edge devices, then bandwidth savings and privacy improvement are achieved, but processing speed and detection accuracy may be compromised
Solution Approach 1:
The patent employs periodic processing by dividing the video stream into fixed temporal windows with overlapping boundaries. This periodic structure allows the system to process video frames at regular intervals rather than continuously, maintaining real-time detection capability while reducing the instantaneous processing load on edge devices, thus preserving processing speed.
Solution Approach 2:
By segmenting the video into manageable temporal windows that can be processed independently and in parallel, the system achieves real-time detection performance on resource-constrained edge devices without compromising detection accuracy or privacy protection.
3Measurement precision
If overlapping windows are used for multimodal feature extraction, then detection accuracy is improved through better temporal coverage, but computational complexity increases
Solution Approach 1:
The patent uses overlapping windows where each frame may be processed multiple times across different windows. This partial redundancy (excessive action) ensures that events spanning multiple frames are captured with high accuracy, while the overlap is carefully controlled to avoid excessive computational complexity. The overlap allows the system to capture temporal context without processing the entire video at maximum resolution.
Solution Approach 2:
The system dynamically adjusts window size and overlap parameters based on the detected event characteristics. By changing these parameters adaptively, the system optimizes the balance between detection accuracy (requiring larger overlap) and computational complexity (requiring smaller overlap), allowing efficient processing on edge devices while maintaining high detection accuracy.
Data Source
AI summary
A method of real-time video event detection includes: obtaining, based on a natural language query, a query vector; performing multimodal feature extraction on a video stream to obtain a video vector, obtaining a similarity score by comparing the query vector to the video vector; comparing the similarity score to a predetermined threshold; and activating, based on the similarity score being above the predetermined threshold, an action trigger. The multimodal feature extraction is performed using a plurality of overlapping windows that include sequential frames of the video stream.