Automatic accident detection using topic models

Kaviani, Razie; Ahmadi, Parvin; Gholampour, Iman

doi:10.1109/iraniancee.2015.7146256

Cited by 8 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[395] uses HMM to detect traffic abnormality, and ref. [396] deployed a topic model to recognise crashes from surveillance videos. However, using DL methods can be used to analyse more complex events and achieve higher performance records.…”

Section: Video-based Event Recognitionmentioning

confidence: 99%

Deep learning serves traffic safety analysis: A forward‐looking review

Razi

Chen

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

This paper explores deep learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasising driving safety for both autonomous vehicles and human‐operated vehicles. A typical processing pipeline is presented, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilisation, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modelling, and anomaly detection. The main goal is to guide traffic analysts to develop their own custom‐built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL‐based algorithms proposed for each step. Existing open‐source tools and public datasets that can help train DL models are also reviewed. To be more specific, exemplary traffic problems are reviewed and required steps are mentioned for each problem. Besides, connections to the closely related research areas of drivers' cognition evaluation, crowd‐sourcing‐based monitoring systems, edge computing in roadside infrastructures, automated driving systems‐equipped vehicles are investigated, and the missing gaps are highlighted. Finally, commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems are reviewed.

show abstract

Section: Video-based Event Recognitionmentioning

confidence: 99%

Deep learning serves traffic safety analysis: A forward‐looking review

Razi

Chen

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…For vehicle-level analysis, when the goal is detecting plain and simple events, conventional methods achieve a reasonable performance. For instance, [326] uses HMM to detect traffic abnormality, and [327] deployed a topic model to recognize crashes from surveillance videos. However, using DL methods can be used to analyze more complex events and achieve higher performance records.…”

Section: Video-based Event Recognitionmentioning

confidence: 99%

Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review

Razi¹,

Chen²,

Li³

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles. We present a typical processing pipeline, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilization, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modeling and anomaly detection. Our main goal is to guide traffic analysts to develop their own custom-built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step. We also review existing open-source tools and public datasets that can help train DL models. To be more specific, we review exemplary traffic problems and mentioned requires steps for each problem. Besides, we investigate connections to the closely related research areas of drivers' cognition evaluation, Crowd-sourcing-based monitoring systems, Edge Computing in roadside infrastructures, ADS-equipped AVs, and highlight the missing gaps. Finally, we review commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems ‡ .

show abstract

“…Since its invention a couple of decades ago, topic modeling-a technique based on co-occurrence data-has routinely been applied to reveal thematic patterns "hidden" in diverse and ever-growing collections of texts [1,2]. Besides text documents, topic models were built to classify image [3], video [4], audio [5], and time-series [6] data, and have become an indispensable tool of business intelligence [7,8]. In the e-commerce industry, this technique has been used increasingly to analyze various documents accumulated online.…”

Section: Introduction and Literature Survey 1backgroundmentioning

confidence: 99%

A Text Segmentation Approach for Automated Annotation of Online Customer Reviews, Based on Topic Modeling

2022

View full text Add to dashboard Cite

Online customer review classification and analysis have been recognized as an important problem in many domains, such as business intelligence, marketing, and e-governance. To solve this problem, a variety of machine learning methods was developed in the past decade. Existing methods, however, either rely on human labeling or have high computing cost, or both. This makes them a poor fit to deal with dynamic and ever-growing collections of short but semantically noisy texts of customer reviews. In the present study, the problem of multi-topic online review clustering is addressed by generating high quality bronze-standard labeled sets for training efficient classifier models. A novel unsupervised algorithm is developed to break reviews into sequential semantically homogeneous segments. Segment data is then used to fine-tune a Latent Dirichlet Allocation (LDA) model obtained for the reviews, and to classify them along categories detected through topic modeling. After testing the segmentation algorithm on a benchmark text collection, it was successfully applied in a case study of tourism review classification. In all experiments conducted, the proposed approach produced results similar to or better than baseline methods. The paper critically discusses the main findings and paves ways for future work.

show abstract

Automatic accident detection using topic models

Cited by 8 publications

References 14 publications

Deep learning serves traffic safety analysis: A forward‐looking review

Deep learning serves traffic safety analysis: A forward‐looking review

Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review

A Text Segmentation Approach for Automated Annotation of Online Customer Reviews, Based on Topic Modeling

Contact Info

Product

Resources

About