Cadence Pattern Identification in Video Analysis
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Solution Overview
Problem
Existing methods for identifying cadence patterns in video analysis are limited to detecting only one type of pattern, are prone to errors in complex sequences, and struggle with noise, slow motion, and freeze frames, requiring initial training and being cumbersome.
Innovation Solution
A system and method using a cadence signature generator module, data analyzer module, and cadence pattern estimator module to encode, analyze, and decode cadence signatures, employing a state machine and Fourier analysis to identify and estimate cadence patterns efficiently without initial training, capable of detecting any cadence pattern including complex sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to identify cadence patterns, then detection of simple patterns is possible, but detection of complex sequences fails with errors
Solution Approach 1:
The patent implements a universal state machine-based algorithm that can detect any type of cadence pattern (2:3, 3:2, 2:2:2:4, complex sequences) through a single unified approach. The state machine transitions through multiple states (S0-S7) to handle different pattern types and sequences, making the system adaptable to any cadence pattern without requiring separate detection methods for each pattern type.
Solution Approach 2:
The patent changes the detection parameter from fixed pattern matching to dynamic state transitions. By using state machine parameters (current state, previous state, field differences) and updating them sequentially, the system can adapt to varying cadence patterns. The algorithm monitors state transitions and field differences to identify patterns dynamically rather than relying on predetermined pattern templates.
2Reliability
If existing methods are employed, then detection of specific patterns is achieved, but the process becomes cumbersome and requires initial training
Solution Approach 1:
The patent implements a self-service detection system where the state machine automatically adapts to the video content without external training. The algorithm independently analyzes field differences, determines state transitions, and identifies cadence patterns on-the-fly. The system serves itself by using the video data to train and adjust its detection logic in real-time, eliminating the need for separate training phases or external calibration.
Solution Approach 2:
The patent extracts only the essential features needed for cadence detection (field differences, state transitions) from the video stream, ignoring unnecessary complexity. By focusing on the core temporal relationships between fields rather than analyzing entire frame content, the system achieves reliable detection with minimal processing complexity. The state machine extracts pattern information from sequential field comparisons without requiring complex training datasets.
3Ease of operation
If conventional cadence detection is used, then processing is straightforward, but the system is prone to noise and freeze frames
Solution Approach 1:
The patent prepares for noise and freeze frames by implementing error-correcting state transitions and validation checks before they cause detection failures. The state machine includes transition validation that verifies whether state changes are consistent with expected cadence patterns, cushioning against erroneous detections caused by noise. The algorithm also incorporates minimum field difference thresholds to cushion against freeze frames where field differences may be abnormally small or zero.
Solution Approach 2:
The patent implements feedback mechanisms where detection results are continuously validated against expected state transitions. When noise or freeze frames cause anomalous field differences, the feedback loop detects inconsistencies in state transition sequences and corrects erroneous pattern identifications. The system monitors the sequence of state transitions and uses feedback from previous transitions to validate current detections, filtering out noise-induced false positives while maintaining simplicity in the overall detection process.
4Reliability
If frame rate conversion techniques are applied, then synchronization between film and video is improved, but complex patterns like 3:2 pull-down create identification challenges
Solution Approach 1:
The patent uses dynamic state transitions to track and identify cadence patterns in converted video streams. The state machine adapts its transition logic based on the detected frame rate conversion pattern, dynamically adjusting to 2:3, 3:2, 2:2:2:4, and other pull-down sequences. By monitoring the temporal dynamics of field differences and state transitions, the algorithm identifies the underlying cadence pattern even in dynamically converted streams, making pattern identification easier despite the complexity introduced by frame rate conversion.
Data Source
AI summary
A system and method for the identification and analysis of cadence pattern is disclosed. The method uses previous and current fields to generate the difference between the field values. The difference of these values along with the field relations is passed to the state machine to generate the state of the top and bottom fields. Based on the top and bottom state the cadence signature is generated and by using the Fourier analysis the principle frequency of repeated cadence pattern signature sequence is identified. Each of the cadence signatures present in the cadence pattern signature sequence is decoded to calculate the pull-down value of the cadence pattern. The pull down value then gives the actual cadence pattern.