Motion Vector Filter Cascade for Real-Time Activity Detection
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Solution Overview
Problem
Existing video analysis methods for activity detection, particularly those using neural networks, require significant computing resources, making real-time processing challenging, especially in resource-constrained devices.
Innovation Solution
A filter cascade approach is employed, comprising a stationary motion noise high-pass filter, short-term low-pass filter, and band-pass filter to process motion vectors, distinguishing between different types of activity and reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are used for activity detection, then detection accuracy is improved, but computing resource consumption increases
Solution Approach 1:
The patent segments the activity detection task into multiple frequency bands using filter banks. Instead of using a single complex neural network to detect all types of activities, the system divides motion vectors into different frequency components (low-frequency stationary noise, mid-frequency activity, high-frequency noise) and processes each band separately with simpler detectors, reducing overall computational requirements while maintaining detection accuracy.
Solution Approach 2:
The patent transforms the input data from raw motion vectors into frequency-domain representations using filter banks. This parameter transformation allows the system to process activities at different temporal scales efficiently, enabling accurate detection with reduced computational complexity by matching detector complexity to the specific frequency band being analyzed.
2Speed
If real-time video processing is performed, then processing speed is improved, but computing resource requirements increase
Solution Approach 1:
The patent applies segmentation by dividing the video processing pipeline into parallel frequency band processing streams. Each band can be processed independently and simultaneously, enabling real-time processing through parallel computation while reducing the computational load on each individual processing unit compared to analyzing the full-spectrum video data sequentially.
3Reliability
If complex filtering operations are applied to motion vectors, then noise reduction is improved, but computational complexity increases
Solution Approach 1:
The patent segments the noise filtering operation into multiple frequency bands using filter banks. Instead of applying a single complex filter to all motion vectors, the system applies simpler band-pass filters to specific frequency ranges, reducing the complexity of each individual filtering operation while achieving comprehensive noise reduction across different temporal scales through the combination of multiple filtered bands.
Data Source
AI summary
According to an example aspect of the present disclosure, there is provided an apparatus configured for applying a stationary motion noise high-pass filter to at least one chronological frame-rate stream of motion vectors, to generate a high-pass filtered stream of motion vectors, to apply a short-term low-pass filter to the high-pass filtered stream of motion vectors to generate a low-pass and high-pass filtered stream of motion vectors, and, for applying a band-pass filter to the low-pass and high-pass filtered stream of motion vectors, to generate a band-pass filtered stream of motion vectors, wherein the band-pass filter is configured to distinguish short-term moving activity and means for detecting whether at least one event is present based on the low-pass and high-pass filtered stream of motion vectors and the band-pass filtered stream of motion vectors.