A deep neural network approach for detecting wrong-way driving incidents on highway roads

Kezebou, Landry; Oludare, Victor; Panetta, Karen; Agaian, Sos С.

doi:10.1117/12.2586039

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Speeding is a type of aggressive driving behavior often due to impatience, anonymity, and traffic congestion [1]. Much work has been done to advance traveler safety in the Intelligent Transportation Systems (ITS) domain; [2] proposed a scalable and efficient algorithm for vehicle model detection; [3] explored video action recognition to tackle the problem of highway incident detection and classification from live surveillance footage by introducing the HWID12 (Highway Incident Detection) dataset; [4] proposed a high precision, deep neural network approach to detecting wrong-way driving (WWD) on highway roads. In maritime border security; [5] proposed the first underwater tracking benchmark dataset; and [6] proposed augmented reality as a tool for 3D navigation.…”

Section: Introductionmentioning

confidence: 99%

Instant-level vehicle speed and traffic density estimation using deep neural network

Jinadu

Oludare

Rajeev

et al. 2023

Multimodal Image Exploitation and Learning 2023

View full text Add to dashboard Cite

The growing network of highway video surveillance cameras generates an immense amount of data that proves tedious for manual analysis. Automated real-time analysis of such data may provide many solutions, including traffic monitoring, traffic incident detection, and smart-city planning. More specifically, assessing traffic speed and density is critical in determining dynamic traffic conditions and detecting slowdowns, traffic incidents, and traffic alerts. However, despite several advancements, there are numerous challenges in estimating vehicle speed and traffic density, which are integral parts of ITS. Some of these challenges include variations in road networks, illumination constraints, weather, structure occlusion, and vehicle user-driving behavior. To address these issues, this paper proposes a novel deep learning-based framework for instant-level vehicle speed and traffic flow density estimation to effectively harness the potential of existing large-scale highway surveillance cameras to assist in real-time traffic analysis. This is achieved using the state-of-the-art region-based Siamese MOT network, SiamMOT which detects and associates object instances for multi-object tracking (MOT), to accurately estimate instant level vehicle speed in live video feeds. The UA-DETRAC dataset is used to train the speed estimation model. Computer simulations show that the proposed framework a) allows the classifying of traffic density into light, medium, or heavy traffic flows, b) is robust to different types of road networks and illuminations without prior road information, and c) shows good performance when compared to current state-of-the-art methods using adequate performance metrics.

show abstract

Section: Introductionmentioning

confidence: 99%

Instant-level vehicle speed and traffic density estimation using deep neural network

Jinadu

Oludare

Rajeev

et al. 2023

Multimodal Image Exploitation and Learning 2023

View full text Add to dashboard Cite

show abstract

“…These rules are, at least in part, established to help avoid responding to many data requests. To help assuage these issues, recently several assistive technologies have been proposed for applications such as incident detection [1,[3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos

Panetta

Kezebou

Oludare

et al. 2021

Self Cite

View full text Add to dashboard Cite

The concept of searching and localizing vehicles from live traffic videos based on descriptive textual input has yet to be explored in the scholarly literature. Endowing Intelligent Transportation Systems (ITS) with such a capability could help solve crimes on roadways. One major impediment to the advancement of fine-grain vehicle recognition models is the lack of video testbench datasets with annotated ground truth data. Additionally, to the best of our knowledge, no metrics currently exist for evaluating the robustness and performance efficiency of a vehicle recognition model on live videos and even less so for vehicle search and localization models. In this paper, we address these challenges by proposing V-Localize, a novel artificial intelligence framework for vehicle search and continuous localization captured from live traffic videos based on input textual descriptions. An efficient hashgraph algorithm is introduced to compute valid target information from textual input. This work further introduces two novel datasets to advance AI research in these challenging areas. These datasets include (a) the most diverse and large-scale Vehicle Color Recognition (VCoR) dataset with 15 color classes—twice as many as the number of color classes in the largest existing such dataset—to facilitate finer-grain recognition with color information; and (b) a Vehicle Recognition in Video (VRiV) dataset, a first of its kind video testbench dataset for evaluating the performance of vehicle recognition models in live videos rather than still image data. The VRiV dataset will open new avenues for AI researchers to investigate innovative approaches that were previously intractable due to the lack of annotated traffic vehicle recognition video testbench dataset. Finally, to address the gap in the field, five novel metrics are introduced in this paper for adequately accessing the performance of vehicle recognition models in live videos. Ultimately, the proposed metrics could also prove intuitively effective at quantitative model evaluation in other video recognition applications. T One major advantage of the proposed vehicle search and continuous localization framework is that it could be integrated in ITS software solution to aid law enforcement, especially in critical cases such as of amber alerts or hit-and-run incidents.

show abstract

Few-Shots Learning for Fine-Grained Vehicle Model Recognition

Kezebou

Oludare

Panetta

et al. 2021

2021 IEEE International Symposium on Technologies for Homeland Security (HST)

View full text Add to dashboard Cite

A deep neural network approach for detecting wrong-way driving incidents on highway roads

Cited by 3 publications

References 0 publications

Instant-level vehicle speed and traffic density estimation using deep neural network

Instant-level vehicle speed and traffic density estimation using deep neural network

Artificial Intelligence for Text-Based Vehicle Search, Recognition, and Continuous Localization in Traffic Videos

Few-Shots Learning for Fine-Grained Vehicle Model Recognition

Contact Info

Product

Resources

About