3D CNN Crowd Behavior Detection Using SlowFast Model
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Solution Overview
Problem
Current methods for detecting abnormal crowd behavior in densely populated areas, such as fights or protests, rely on inaccurate human reports or require constant monitoring of surveillance cameras, leading to potential misses and high operational costs due to the need for continuous human supervision.
Innovation Solution
An automated method using a three-step process: data pre-processing, feature extraction via a 3D CNN, and post-processing to integrate and synthesize information for timely warnings, which includes cutting video clips, resizing, and using a variant of the SlowFast model to predict abnormal behavior from surveillance camera feeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation of surveillance cameras is used, then detection accuracy is improved, but operational cost and time consumption increase due to constant human monitoring
Solution Approach 1:
The patent replaces manual human observation with an automated computer-based detection system that processes video feeds from surveillance cameras. The system automatically analyzes crowd behavior patterns, detects abnormal activities, and generates alerts without requiring continuous human monitoring, thereby maintaining detection accuracy while eliminating time consumption and operational costs associated with manual surveillance.
2Reliability
If more supervisory personnel are deployed, then detection reliability is improved, but operational cost increases due to staffing requirements
Solution Approach 1:
The patent implements a self-service detection system where the computer automatically performs surveillance, analysis, and alert generation functions that would otherwise require human operators. The system monitors multiple camera feeds simultaneously, processes video data through algorithms, and autonomously identifies abnormal crowd behaviors, eliminating the need for additional supervisory personnel while maintaining or improving detection reliability.
3Loss of information
If human reports are used for detection, then information gathering is improved, but accuracy deteriorates due to human bias and trauma
Solution Approach 1:
The patent replaces human reporting mechanisms with automated computer-based analysis of surveillance video feeds. Instead of relying on witnesses who may be traumatized or biased, the system objectively processes visual data from multiple cameras, automatically identifies abnormal crowd behaviors through pattern recognition algorithms, and generates unbiased detection results, thereby improving both information gathering and measurement precision.
Data Source
AI summary
This invention proposes a method of crowd abnormal behavior detection from video using artificial intelligence, includes three steps: step 1: Data-preprocessing; step 2: Feature extraction and abnormal prediction using a three-dimensional convolution neural network (3D CNN), step 3: Post-processing and synthesizing information to issue warning.