Rear Vehicle Detection and Tracking for Lane Change Assist

Liu, Wei; Wen, Xuezhi; Duan, Bobo; Yuan, Huai; Wang, Nan

doi:10.1109/ivs.2007.4290123

Cited by 81 publications

(42 citation statements)

References 10 publications

(10 reference statements)

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“…In [24], the camera was positioned looking backward, out of the rear windshield. The application was detection of the front faces of following vehicles, to advise the driver on the safety of ego lane change.…”

Section: A Monocular Vehicle Detectionmentioning

confidence: 99%

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

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

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Abstract-This paper provides a review of the literature in on-road vision-based vehicle detection, tracking, and behavior understanding. Over the past decade, vision-based surround perception has progressed from its infancy into maturity. We provide a survey of recent works in the literature, placing vision-based vehicle detection in the context of sensor-based on-road surround analysis. We detail advances in vehicle detection, discussing monocular, stereo vision, and active sensor-vision fusion for on-road vehicle detection. We discuss vision-based vehicle tracking in the monocular and stereo-vision domains, analyzing filtering, estimation, and dynamical models. We discuss the nascent branch of intelligent vehicles research concerned with utilizing spatiotemporal measurements, trajectories, and various features to characterize on-road behavior. We provide a discussion on the state of the art, detail common performance metrics and benchmarks, and provide perspective on future research directions in the field.

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Section: A Monocular Vehicle Detectionmentioning

confidence: 99%

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

Sivaraman

Trivedi

2013

IEEE Trans. Intell. Transport. Syst.

802

322

View full text Add to dashboard Cite

show abstract

“…For example, Histogram of oriented gradient (HOG) features [6] have been used in a number of studies [7]. In [8], the symmetry of the HOG features extracted in a given image patch, along with the HOG features themselves, was used for vehicle detection.…”

Section: Related Workmentioning

confidence: 99%

“…These features are feed to the classifiers to perform the vehicle detection process. Support vector machines (SVMs) have been widely used with HOG features for vehicle detection [7], [9]. The HOG-SVM formulation was extended to detect and calculate vehicle orientation using multiplicative kernels in [17].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for On-road Vehicle Detection and Tracking

Jaafari¹,

Ansari²,

Koutti³

et al. 2016

ijacsa

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Abstract-On the basis of a necessary development of the road safety, vision-based vehicle detection techniques have gained an important amount of attention. This work presents a novel vehicle detection and tracking approach, and structured based on a vehicle detection process starting from, images or video data acquired from sensors installed on board of the vehicle, to vehicle detection and tracking. The features of the vehicle are extracted by the proposed GIST image processing algorithm, and recognized by the state-of-art Support Vectors Machine classifier. The tracking process was performed based on edge features matching approach. The Kalman filter was used to correct the measurements. Extensive experiments were carried out on real image data validate that it is promising to employ the proposed approach for on road vehicle detection and tracking.

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“…Within the defined ROI, based on the common features that vehicles always cause shadow on the road [18], the shadow underneath the vehicles is detected. Then, the vehicle bottom lines, where the vehicle and road meet, are extracted.…”

Section: Hypothesis Generationmentioning

confidence: 99%

“…One important feature in common is that they always cause shadow on the road surface. Vehicle candidates can be extracted by detecting the shadows underneath vehicles [18]. Potential shaded areas are the region with significant darker intensities than the road.…”

Section: Hypothesis Generation: Shadow Detectionmentioning

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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Rear Vehicle Detection and Tracking for Lane Change Assist

Cited by 81 publications

References 10 publications

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

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

A Novel Approach for On-road Vehicle Detection and Tracking

Improved Genetic Algorithm Optimization for Forward Vehicle Detection Problems

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