VIDEO BASED UNSUPERVISED LEARNING OF PERIODIC SIGNALS
The disclosure introduces systems, devices, methods, and instructions for autonomously identifying recurring patterns (e.g., pulse, respiration) in video content using unsupervised learning. The embodiments overcome the limitations of traditional supervised methods that require extensive labeled datasets, particularly for detecting subtle periodic signals like heart rate and respiration. The disclosure utilizes feature extraction, clustering algorithms, and validation to analyze video data for these temporal patterns, offering potential applications in various fields such as border or gate security, deception detection, healthcare, and entertainment without the need for manual annotation.
1 . A system for detecting periodic signals within video data, comprising:
a video processing unit configured to receive video data as input;
a feature extraction module configured to analyze frames of the video data to identify patterns indicative of periodicity, the feature extraction module comprising spatial and temporal filtering components; and
an unsupervised learning module to identify and group one or more periodic events based.
2 . The system of claim 1 , wherein the feature extraction module further comprises a frequency domain transformation component to derive features indicative of repeated events.
3 . The system of claim 1 , wherein the unsupervised learning module further includes a dimensionality reduction component to manage computational complexity and improve data interpretability.
4 . The system of claim 1 , further comprising a visualization interface for displaying interpretations of detected periodic signals.
5 . The system of claim 1 , wherein the system is implemented at a border crossing.
6 . The system of claim 1 , wherein the system is implemented at an access control point.
7 . A method for detecting periodic signals within video data, comprising:
receiving video data as input through a video processing unit;
extracting features from frames of the video data using a feature extraction module to identify patterns indicative of periodicity; and
autonomously learning periodic characteristics from the extracted features using an unsupervised learning module.
8 . The method of claim 7 , wherein extracting features comprises applying spatial and temporal filtering techniques.
9 . The method of claim 7 , wherein autonomously learning involves employing clustering algorithms to group similar periodic events.
10 . The method of claim 7 , further comprising reducing dimensionality of the features to manage computational complexity and improve interpretability.
11 . The method of claim 7 , further comprising providing a visualization of the detected periodic signals through a graphical interface.
12 . The method of claim 7 , wherein the video data comprises surveillance footage, medical imaging, or multimedia content.
13 . The method of claim 7 , wherein the video data comprises video of a border crossing or an access control point.
14 . A system for real-time video analysis of periodic signals, comprising:
a video processing unit configured to intake real-time video feed;
a hardware-accelerated feature extraction module configured to perform spatial and temporal filtering on the video feed;
an unsupervised learning component configured to identify and group periodic events within the video feed; and
a dynamic validation mechanism for continuous assessment of periodic event detection accuracy.
15 . The system of claim 14 , wherein the hardware-accelerated feature extraction module operates on a GPU or TPU for enhanced performance.