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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Ensemble deep learning approach for traffic video analytics in edge computing.

Malathy Sathyamoorthy¹, Vani Rajasekar², Sathya Krishnamoorthi³

¹Department of Information Technology, KPR Institute of Engineering and Technology, Coimbatore, India. ksmalathy@gmail.com.

Scientific Reports

|January 3, 2026

Summary

This study introduces a hybrid edge computing model using Tiny YOLO and YOLOR for real-time vehicle detection. The system enhances traffic surveillance accuracy and processing speed, enabling seamless traffic flow control.

Keywords:

Deep learning Ensemble learning F-RNN Tiny YOLO Video analytics YOLOR

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Area of Science:

Computer Vision
Artificial Intelligence
Traffic Engineering

Background:

Real-time traffic monitoring is crucial for effective traffic control.
Current video analytics for traffic surveillance face challenges with latency (cloud) or accuracy (edge).
YOLO algorithms, particularly Tiny YOLO, offer efficient object detection for resource-constrained environments.

Purpose of the Study:

To develop a novel hybrid model for vehicle detection and classification at the edge layer.
To improve the accuracy and processing speed of real-time traffic surveillance.
To enable seamless traffic flow control through enhanced decision-making.

Main Methods:

A hybrid model combining Tiny YOLO and YOLOR was developed for edge-based vehicle detection and classification.
The model processes video frames at the edge to reduce latency and enhance real-time analysis.
Ensemble Learning in Traffic Video Analytics (ELITVA) with F-RNN was used for decision-making based on traffic volume data.

Main Results:

The hybrid model demonstrated significant improvements in precision (+13.8%), accuracy (+4.8%), recall (+17.4%), and F1 score (+19.9%).
Frame rate processing increased by 12.8% compared to existing systems.
Experimental results on a drone dataset captured at road signals validated the model's effectiveness.

Conclusions:

The proposed hybrid edge computing model offers a superior solution for real-time traffic surveillance.
This approach effectively addresses the limitations of cloud and edge-based video analytics.
The system facilitates efficient road traffic control and management through accurate and rapid analysis.