Predictive Security Camera Clustering for Edge Anomaly Forecasting
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Solution Overview
Problem
Existing anomaly detection systems are unable to generate future predictions regarding time, location, and classes of objects captured by cameras, are computationally complex, and require expensive hardware like GPUs, limiting their use on edge devices.
Innovation Solution
A predictive security camera system utilizing a convolutional encoder, primary and secondary clustering modules, and a predictive analysis module to generate future predictions on edge devices by clustering object features and contextual data, allowing for anomaly detection and reduced computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep CNN with many layers is used for object recognition, then manufacturing precision is improved, but device complexity increases and use of energy increases
Solution Approach 1:
The patent segments the video stream processing into discrete frame-level object detection tasks. Instead of using a complex deep CNN that processes entire video sequences, the system divides the problem into individual frame analyses, each handled by a lighter-weight object recognition model. This segmentation reduces overall system complexity while maintaining recognition accuracy through frame-by-frame processing.
Solution Approach 2:
The system dynamically adjusts its processing approach based on the specific frame being analyzed. Rather than applying a static, uniformly complex deep CNN to all frames, the system adapts its object recognition strategy to the particular characteristics of each frame, allowing for more efficient processing that maintains accuracy without requiring the full complexity of a deep network for every case.
2Manufacturing precision
If deep CNN with many layers is used for object recognition, then manufacturing precision is improved, but use of energy increases
Solution Approach 1:
By segmenting video processing into individual frame analyses rather than continuous deep CNN processing of entire sequences, the system reduces cumulative energy consumption. Each frame is processed independently with a more efficient model, avoiding the excessive energy requirements of running deep networks continuously on all video data while maintaining recognition accuracy through focused frame-level analysis.
Solution Approach 2:
The patent employs lighter-weight, less energy-intensive object recognition models that can be deployed on resource-constrained devices. Instead of relying on power-hungry deep CNNs, the system uses more efficient algorithms that consume less energy per frame, making the system viable for battery-operated portable devices where energy conservation is critical.
3Measurement precision
If existing anomaly detection systems are used, then object detection is improved, but device complexity increases
Solution Approach 1:
The patent extracts and focuses specifically on temporal anomalies rather than implementing comprehensive anomaly detection systems. By isolating the temporal dimension of anomaly detection and addressing only that specific aspect, the system achieves effective anomaly identification without the complexity of full-scale anomaly detection systems that would analyze multiple dimensions simultaneously.
Solution Approach 2:
The system dynamically identifies temporal patterns and anomalies based on the specific temporal characteristics of the video stream. Rather than using a static, complex anomaly detection system, the approach adapts to the temporal dynamics of the data, identifying anomalies through time-based pattern recognition that is simpler than comprehensive multi-dimensional anomaly detection while maintaining effectiveness for temporal anomalies.
4Measurement precision
If existing anomaly detection systems are used, then object detection is improved, but use of energy increases
Solution Approach 1:
By extracting and focusing only on temporal anomaly detection rather than implementing comprehensive anomaly detection, the system significantly reduces energy consumption. The specialized temporal approach requires less computational resources than general-purpose anomaly detection systems, making it energy-efficient for portable devices while maintaining accuracy for time-based anomalies.
Solution Approach 2:
The patent employs energy-efficient algorithms for temporal anomaly detection that can run on battery-operated devices. Instead of using power-intensive comprehensive anomaly detection systems, the system uses lighter-weight temporal analysis methods that consume less energy per unit time, enabling sustained operation on portable devices with limited power resources.
Data Source
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AI summary
A predictive security camera system includes a camera, an object recognizer for detecting an object in a current frame, a convolutional encoder for generating a feature representation of the current frame, a primary clustering module for clustering the feature representation in primary clusters of the detected object, a vector generation module for generating an information vector that includes a set of variables such as primary class, sub-class, and one or more external factors associated with the feature representation, an anomaly clustering module for clustering each information vector into one or more secondary clusters wherein each variable in the information vector is a clustering dimension, and a predictive analysis module for receiving at least one variable of an information vector as an input, and predicting at least one another variable of the information vector based on one or more previous secondary clusters formed by the anomaly clustering module.