Hybrid People Detection System for Crowd Analysis
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Solution Overview
Problem
Current AI computer vision technologies face challenges in accurately and efficiently detecting, tracking, and counting individuals in crowded scenes from video streams, particularly due to limitations in model training and the need for balanced speed and accuracy in person detection and tracking.
Innovation Solution
A hybrid people detector and counter system is developed, utilizing a modified Yolo-based end-to-end deep learning model (Yolov7) with a crafted training approach that incorporates local population data for enhanced detection, tracking, and re-identification, focusing on body and face features for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If one stage detection approach is used, then speed and efficiency are improved, but accuracy and precision deteriorate
Solution Approach 1:
The detection process is divided into two distinct stages: a fast one-stage detector for rapid object localization and a more accurate two-stage detector for precise identification. This segmentation allows the system to leverage the speed of one-stage detectors while achieving the accuracy of two-stage detectors through sequential processing.
Solution Approach 2:
The system dynamically adjusts the detection pipeline based on operational requirements. The hybrid architecture enables flexible switching between detection modes, allowing the system to optimize performance by combining the rapid initial detection of one-stage models with the refined precision of two-stage models in a coordinated manner.
2Measurement precision
If two stage detection approach is used, then accuracy and precision are improved, but speed deteriorates
Solution Approach 1:
The detection process is divided into two distinct stages: a fast one-stage detector for rapid object localization and a more accurate two-stage detector for precise identification. This segmentation allows the system to leverage the speed of one-stage detectors while achieving the accuracy of two-stage detectors through sequential processing.
Solution Approach 2:
The one-stage detector performs preliminary detection to identify potential objects of interest quickly, filtering out obvious non-targets before the more computationally intensive two-stage detector processes the data. This preliminary action reduces the workload for the second stage while maintaining overall accuracy.
3Measurement precision
If AI model training is enhanced with local population data, then detection accuracy is improved, but model complexity increases
Solution Approach 1:
The system incorporates local population characteristics and specific features into the training data, making the model adapted to local conditions. This local quality enhancement improves detection accuracy for specific demographic groups while maintaining overall system performance through targeted rather than universal complexity increases.
Data Source
AI summary
Provided are computer-implemented technologies for crowd analysis. The technologies process input video footages of a crowd to detect people from the input, uniquely identify the detected people, track and count them across all the frames throughout the footages, classify them based on features of the identified people such as detected gender of the identified people. The technologies use customarily trained AI models that are specifically trained and retrained for detecting people in the local population that typically wear Arab style clothing. The learned models, through the training, quality controlling and retraining, attain the prediction capability for accurately detecting, identifying and classifying people from crowd scenes. The learned model, then, is applied to crowd analysis to provide detection and classification report of the crowd, which can be used for deeper analysis to fill various business/institutional needs.