On-road vehicle detection using optical sensors: a review

Sun, Zehang; Bebis, George; Miller, Ronald H.

doi:10.1109/itsc.2004.1398966

Cited by 177 publications

(160 citation statements)

References 31 publications

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“…Various sensors can also be used for vehicle detection systems. Literature for video image or vision-based sensors has been reviewed by Sun et al [25]. Moving vehicle detection and classification system is popular, with video image or vision-based sensors being used on a large scale.…”

Section: Use Of Instrumented Vehicles For Traffic Data Extractionmentioning

confidence: 99%

Characteristics of lateral vehicular interactions in heterogeneous traffic with weak lane discipline

Budhkar

Maurya

2017

J. Mod. Transport.

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Heterogeneous traffic conditions prevail in developing countries. Vehicles maintain weak lane discipline which increases lateral interactions of vehicles significantly. It is necessary to study these interactions in the form of maintained lateral gaps for modeling this traffic scenario. This paper aims at determining lateral clearances maintained by different vehicle types while moving in a heterogeneous traffic stream during overtaking. These data were collected using an instrumented vehicle which runs as a part of the stream. Variation of obtained clearance with average speed of interacting vehicles is studied and modeled. Different instrumented vehicles of various types are developed using (1) ultrasonic sensors fixed on both sides of vehicle, which provide inter-vehicular lateral distance and relative speed; and (2) GPS device with cameras, which provides vehicle type and speed of interacting vehicles. They are driven on different roads in six cities of India, to measure lateral gaps maintained with different interacting vehicles at different speeds. Relationships between lateral gaps and speed are modeled as regression lines with positive slopes and beta-distributed residuals. Nature of these graphs (i.e., slopes, intercepts, residuals) are also evaluated and compared for different interacting vehicle-type pairs. It is observed that similar vehicle pairs maintain less lateral clearance than dissimilar vehicle pairs. If a vehicle interacts with two vehicles (one on each side) simultaneously, lateral clearance is reduced and safety of the vehicles is compromised. The obtained relationships can be used for simulating lateral clearance maintaining behavior of vehicles in heterogeneous traffic.

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Section: Use Of Instrumented Vehicles For Traffic Data Extractionmentioning

confidence: 99%

Characteristics of lateral vehicular interactions in heterogeneous traffic with weak lane discipline

Budhkar

Maurya

2017

J. Mod. Transport.

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“…However, exhaustive search of weeds on the complete image is prohibitive for real-time application. To solve this problem, in the literature, most object detection methods are divided into two stages according to attentional processes [12]: a. Development of hypotheses: the system provides a very simple and quick way of potential positions of objects to be detected in order to restrict the search field.…”

Section: Figure 1 Experimental Setupmentioning

confidence: 99%

Real Time Weed Detection using a Boosted Cascade of Simple Features

Tannouche¹,

Sbai²,

Rahmoune³

et al. 2016

IJECE

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Weed detection is a crucial issue in precision agriculture. In computer vision, variety of techniques are developed to detect, identify and locate weeds in different cultures. In this article, we present a real-time new weed detection method, through an embedded monocular vision. Our approach is based on the use of a cascade of discriminative classifiers formed by the Haar-like features. The quality of the results determines the validity of our approach, and opens the way to new horizons in weed detection. Keyword:AdaBoost INTRODUCTIONWeed control is a key element in precision agriculture. Chemical weed control is widely practiced to reduce infection rates and improve crops. In this regard, recent computer vision research has spawned several effective techniques for the detection and / or classification of weeds. In [1] the authors have developed a rapid shape descriptor for the selection of weeds. In [2] the authors elaborated a multilayer perceptron-based technology to real time detection of weeds in growing onions. In [3] a hybrid of support vector machine (SVM) with Radial basis function (RBF) are used to identification of four major types of crop pest which occurs on teff, wheat, sorghum, barley and maize. In [4] the authors proposes a novel angular texture pattern (ATP) extraction method for crop and weed discrimination to overcome the defectsof background complexity and variation of illumination in the agricultural scene. In [5] the authors introduce a new weed detection system using fractal-based low cost commodity hardware Raspberry Pi.In this article, we present a new method for detecting adventitious board by monocular vision. The spatial information contained in the images from the field is described by the rectangular filters called Haar-like features [6]-7]. Then, this discriminative descriptor utilizes a concept of cascade similar to that proposed in [8][9] to find weeds in cultivated land. The originality of our approach lies in the study and evaluation of this technique of artificial vision, which is widely known and recognized in object recognition in the area of weed detection.

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“…Research in tracking plays a key role in understanding motion and structure of objects. It finds numerous applications including surveillance, 11 human-computer interaction, 12 traffic pattern analysis, 13 recognition, 14 medical image processing, 15 to name a few. Although object tracking has been studied for several decades, and numerous tracking algorithms have been proposed for different tasks, it remains a very challenging problem.…”

Section: Introductionmentioning

confidence: 99%

An experimental comparison of online object-tracking algorithms

et al. 2011

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This paper reviews and evaluates several state-of-the-art online object tracking algorithms. Notwithstanding decades of efforts, object tracking remains a challenging problem due to factors such as illumination, pose, scale, deformation, motion blur, noise, and occlusion. To account for appearance change, most recent tracking algorithms focus on robust object representations and effective state prediction. In this paper, we analyze the components of each tracking method and identify their key roles in dealing with specific challenges, thereby shedding light on how to choose and design algorithms for different situations. We compare state-of-the-art online tracking methods including the IVT, 1 VRT, 2 FragT, 3 BoostT, 4 SemiT, 5 BeSemiT, 6 L1T, 7 MILT, 8 VTD 9 and TLD 10 algorithms on numerous challenging sequences, and evaluate them with different performance metrics. The qualitative and quantitative comparative results demonstrate the strength and weakness of these algorithms.

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On-road vehicle detection using optical sensors: a review

Cited by 177 publications

References 31 publications

Characteristics of lateral vehicular interactions in heterogeneous traffic with weak lane discipline

Characteristics of lateral vehicular interactions in heterogeneous traffic with weak lane discipline

Real Time Weed Detection using a Boosted Cascade of Simple Features

An experimental comparison of online object-tracking algorithms

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