Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Sivaraman, Sayanan; Trivedi, Mohan M.

doi:10.1109/tits.2013.2266661

Cited by 792 publications

(331 citation statements)

References 188 publications

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“…Extensive studies have been proposed for vision-based FCW systems [4] [5] [6] [7]. Sun et al [4] and Sivaraman et al [5] generally divided the vision-based vehicle detection methods into three categories: knowledge-based, motionbased, and stereo-based methods.…”

Section: Introductionmentioning

confidence: 99%

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Vision-Based Vehicle Detection for a Forward Collision Warning System

Tseng¹,

Huang²

2017

WJET

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A weather-adaptive forward collision warning (FCW) system was presented by applying local features for vehicle detection and global features for vehicle verification. In the system, horizontal and vertical edge maps are separately calculated. Then edge maps are threshold by an adaptive threshold value to adapt the brightness variation. Third, the edge points are linked to generate possible objects. Fourth, the objects are judged based on edge response, location, and symmetry to generate vehicle candidates. At last, a method based on the principal component analysis (PCA) is proposed to verify the vehicle candidates. The proposed FCW system has the following properties: 1) the edge extraction is adaptive to various lighting condition; 2) the local features are mutually processed to improve the reliability of vehicle detection; 3) the hierarchical schemes of vehicle detection enhance the adaptability to various weather conditions; 4) the PCA-based verification can strictly eli-minate the candidate regions without vehicle appearance.

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Section: Introductionmentioning

confidence: 99%

“…Sun et al [4] and Sivaraman et al [5] generally divided the vision-based vehicle detection methods into three categories: knowledge-based, motionbased, and stereo-based methods. Knowledge-based methods integrate many features of vehicles to recognize preceding vehicles in images.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Vehicle Detection for a Forward Collision Warning System

Tseng¹,

Huang²

2017

WJET

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“…In which, reasonable and effective feature extraction is important for the detection phase. For this purpose, various prior knowledge such as shadow, symmetry, color, edge and texture are used [6][7][8][9][10]. In ref [6], shadow and edge information are used.…”

Section: Introductionmentioning

confidence: 99%

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

et al. 2015

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Automated forward vehicle detection is an integral component of many advanced driver-assistance systems. The method based on multi-visual information fusion, with its exclusive advantages, has become one of the important topics in this research field. During the whole detection process, there are two key points that should to be resolved. One is to find the robust features for identification and the other is to apply an efficient algorithm for training the model designed with multi-information. This paper presents an adaptive SVM (Support Vector Machine) model to detect vehicle with range estimation using an on-board camera. Due to the extrinsic factors such as shadows and illumination, we pay more attention to enhancing the system with several robust features extracted from a real driving environment. Then, with the introduction of an improved genetic algorithm, the features are fused efficiently by the proposed SVM model. In order to apply the model in the forward collision warning system, longitudinal distance information is provided simultaneously. The proposed method is successfully implemented on a test car and evaluation experimental results show reliability in terms of both the detection rate and potential effectiveness in a real-driving environment.

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“…A recent survey shows that vehicle detection problem was considered in many studies [22]. This problem has been addressed by employing different sensors (e.g.…”

Section: Chapter 2 Literature Reviewmentioning

confidence: 99%

“…This problem has been addressed by employing different sensors (e.g. stereo/monocular cameras, radar, or lidar), or using sensor-fusion approaches [22]. For our project, we were not able to install any devices such as lidar or radar on the trains because of safety concerns related to signal interference between these devices and the on-board systems of the trains.…”

Section: Chapter 2 Literature Reviewmentioning

confidence: 99%

Video Analytics for the Detection of Near-Miss Incidents at Railway Level Crossings and Signal Passed at Danger Events

Aminmansour¹

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The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made. Currently most of the data for near-miss and SPAD incidents are collected from the reports made by train drivers. There is a doubt that all of the incidents that occurred are actually reported by train drivers, due to the subjectivity of near-miss events, and also disciplinary actions that v might be taken against train drivers, if they are responsible for any incidents.To identify the causal factors of railway collisions, the Cooperative Research Centre for Rail Innovation has initiated a research project to develop an automated data collection system to support railway collision analysis. The system collects data from trains' on-board computers, GPS receivers, and forward facing cameras. This thesis presents computer vision algorithms for automatically detecting near-miss and SPAD incidents from train cameras as part of this project.To automatically detect near-miss events from videos, we localize vehicles in each frame and calculate the distance of the vehicles to the railway. If the distance of a vehicle to the railway is less than a pre-defined distance, the event will be flagged as a near-miss incident for further analysis. Our proposed near-miss detection system consists of railway detection and segmentation, and vehicle detection and localization algorithms. We employ several computer vision techniques including image segmentation, convolutional neural networks, and 3D geometry reconstruction.In order to automatically detect SPAD incidents from videos, we detect railway signals as well as the state of the signals in each frame. An event will be flagged as a SPAD if a train passes a red signal. Our approach for detecting SPAD events consists of using morphological operations, different colour spaces, 3D geometry reconstruction, segmentation algorithms, and machine learning techniques.This thesis introduces algorithms for automatically detecting level crossing near-miss and SPAD incidents from video data obtained by forward facing cameras of trains. The accuracy rate of our near-miss detection system for detecting and segmenting railways on our railway dataset is 96% for day-time, and 93% for night-time videos. The system has 75% of precision and 81% of recall for detecting vehicles on this dataset. We compared our vehicle detector algorithm with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. The recall rate of our algorithm is 29% higher than what can be achieved with DPM or R-CNN detectors on our railway dataset. We also present the results of detecting railway signals on more than 5,000 frames for our SPAD detection system. This thesis opens avenues for helping to identify the causal factors of train collisions.vi

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Looking at Vehicles on the Road: A Survey of Vision-Based Vehicle Detection, Tracking, and Behavior Analysis

Cited by 792 publications

References 188 publications

Vision-Based Vehicle Detection for a Forward Collision Warning System

Vision-Based Vehicle Detection for a Forward Collision Warning System

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

Video Analytics for the Detection of Near-Miss Incidents at Railway Level Crossings and Signal Passed at Danger Events

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