A Self-Adaptive Automatic Incident Detection System for Road Surveillance Based on Deep Learning
César Bartolomé-Hornillos, Luis M. San-José-Revuelta, Javier M. Aguiar-Pérez, Carlos García-Serrada, Eduardo Vara-Pazos, Pablo Casaseca-de-la-Higuera
TL;DR
This paper introduces a low-cost, self-adaptive deep learning system for detecting road incidents in real-time using affordable devices.
Contribution
The novelty lies in the system's self-adaptability to changing road conditions and low computational requirements for real-time incident detection.
Findings
The system processes 80 video frames in 12 seconds, covering 400 meters of road.
It achieves 2–7% higher accuracy than previous methods in both automatic and semi-automatic modes.
The classifier uses only 2.3 MBytes of GPU memory, enabling use in low-cost devices.
Abstract
We present an automatic road incident detector characterised by a low computational complexity for easy implementation in affordable devices, automatic adaptability to changes in scenery and road conditions, and automatic detection of the most common incidents (vehicles with abnormal speed, pedestrians or objects falling on the road, vehicles stopped on the shoulder, and detection of kamikaze vehicles). To achieve these goals, different tasks have been addressed: lane segmentation, identification of traffic directions, and elimination of unnecessary objects in the foreground. The proposed system has been tested on a collection of videos recorded in real scenarios with real traffic, including areas with different lighting. Self-adaptability (plug and play) to different scenarios has been tested using videos with significant scene changes. The achieved system can process a minimum of 80…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Autonomous Vehicle Technology and Safety · Advanced Neural Network Applications