# SigSegment: A Signal-Based Segmentation Algorithm for Identifying Anomalous Driving Behaviours in Naturalistic Driving Videos

arXiv: https://arxiv.org/abs/2304.09247 · HTML: https://mlsanity.com/paper/2304.09247/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 6 of 6)

- [A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees](https://mlsanity.com/paper/2104.06856/) (2021)
- [MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction with Automation](https://mlsanity.com/paper/1711.06976/) (2017)
- [Mobile Sensing for Multipurpose Applications in Transportation](https://mlsanity.com/paper/2106.10733/) (2021)
- [A Region-Based Deep Learning Approach to Automated Retail Checkout](https://mlsanity.com/paper/2204.08584/) (2022)
- [Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification](https://mlsanity.com/paper/2211.06463/) (2022)
- [AI-Based Framework for Understanding Car Following Behaviors of Drivers in A Naturalistic Driving Environment](https://mlsanity.com/paper/2301.09315/) (2023)

## Abstract (arXiv)

In recent years, distracted driving has garnered considerable attention as it continues to pose a significant threat to public safety on the roads. This has increased the need for innovative solutions that can identify and eliminate distracted driving behavior before it results in fatal accidents. In this paper, we propose a Signal-Based anomaly detection algorithm that segments videos into anomalies and non-anomalies using a deep CNN-LSTM classifier to precisely estimate the start and end times of an anomalous driving event. In the phase of anomaly detection and analysis, driver pose background estimation, mask extraction, and signal activity spikes are utilized. A Deep CNN-LSTM classifier was applied to candidate anomalies to detect and classify final anomalies. The proposed method achieved an overlap score of 0.5424 and ranked 9th on the public leader board in the AI City Challenge 2023, according to experimental validation results.