# DeepSegmenter: Temporal Action Localization for Detecting Anomalies in Untrimmed Naturalistic Driving Videos

arXiv: https://arxiv.org/abs/2304.08261 · HTML: https://mlsanity.com/paper/2304.08261/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 13 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [X3D: Expanding Architectures for Efficient Video Recognition](https://mlsanity.com/paper/2004.04730/) (2020)
- [AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions](https://mlsanity.com/paper/1705.08421/) (2017)
- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [Rethinking the Faster R-CNN Architecture for Temporal Action Localization](https://mlsanity.com/paper/1804.07667/) (2018)
- [End-to-end Temporal Action Detection with Transformer](https://mlsanity.com/paper/2106.10271/) (2021)
- [TURN TAP: Temporal Unit Regression Network for Temporal Action Proposals](https://mlsanity.com/paper/1703.06189/) (2017)
- [A Vision-based System for Traffic Anomaly Detection using Deep Learning and Decision Trees](https://mlsanity.com/paper/2104.06856/) (2021)
- [The 6th AI City Challenge](https://mlsanity.com/paper/2204.10380/) (2022)
- [Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driver](https://mlsanity.com/paper/2204.08096/) (2022)
- [Mobile Sensing for Multipurpose Applications in Transportation](https://mlsanity.com/paper/2106.10733/) (2021)
- [Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification](https://mlsanity.com/paper/2211.06463/) (2022)
- [Video-based Human Action Recognition using Deep Learning: A Review](https://mlsanity.com/paper/2208.03775/) (2022)
- [Comparative Analysis of Machine Learning Models for Predicting Travel Time](https://mlsanity.com/paper/2111.08226/) (2021)
- [AFO-TAD: Anchor-free One-Stage Detector for Temporal Action Detection](https://mlsanity.com/paper/1910.08250/) (2019)
- [Identifying the Factors that Influence Urban Public Transit Demand](https://mlsanity.com/paper/2111.09126/) (2021)

## Built on by (most cited first, 6 of 6)

- [The 7th AI City Challenge](https://mlsanity.com/paper/2304.07500/) (2023)
- [Integrating Travel Behavior Forecasting and Generative Modeling for Predicting Future Urban Mobility and Spatial Transformations](https://mlsanity.com/paper/2503.21158/) (2025)
- [Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets](https://mlsanity.com/paper/2404.10078/) (2024)
- [An Improved ResNet50 Model for Predicting Pavement Condition Index (PCI) Directly from Pavement Images](https://mlsanity.com/paper/2504.18490/) (2025)
- [Prompt-Guided Spatial Understanding with RGB-D Transformers for Fine-Grained Object Relation Reasoning](https://mlsanity.com/paper/2510.11996/) (2025)
- [Monitoring Viewer Attention During Online Ads](https://mlsanity.com/paper/2504.06237/) (2025)

## Abstract (arXiv)

Identifying unusual driving behaviors exhibited by drivers during driving is essential for understanding driver behavior and the underlying causes of crashes. Previous studies have primarily approached this problem as a classification task, assuming that naturalistic driving videos come discretized. However, both activity segmentation and classification are required for this task due to the continuous nature of naturalistic driving videos. The current study therefore departs from conventional approaches and introduces a novel methodological framework, DeepSegmenter, that simultaneously performs activity segmentation and classification in a single framework. The proposed framework consists of four major modules namely Data Module, Activity Segmentation Module, Classification Module and Postprocessing Module. Our proposed method won 8th place in the 2023 AI City Challenge, Track 3, with an activity overlap score of 0.5426 on experimental validation data. The experimental results demonstrate the effectiveness, efficiency, and robustness of the proposed system.