Temporal consistent real-time stereo for intelligent vehicles

Ansari, Mohamed El; Mousset, Stéphane; Bensrhair, Abdelaziz

doi:10.1016/j.patrec.2010.03.023

Cited by 26 publications

(25 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To extract edges, we had used canny edge operator [42] for the reason that it yields continuous edge curves which are vital to the proposed method and for its detection precision. The algorithm of association proposed in [43] is used to find relationships between consecutive frames. Edge curve matching process is applied based on these relationships to find correspondences between edge curves of two consecutive images.…”

Section: B Vehicle Trackingmentioning

confidence: 99%

“…We begin by describing the association algorithm used to find relationship between consecutive images. More details can be found in [43]. Let us consider two edge points P [43], [44].…”

Section: ) Edge Curves Matching Processmentioning

confidence: 99%

“…More details can be found in [43]. Let us consider two edge points P [43], [44]. Based on these associate edge points computed used the association algorithm described above, we match the edge curves of each two consecutive images.…”

Section: ) Edge Curves Matching Processmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Approach for On-road Vehicle Detection and Tracking

Jaafari¹,

Ansari²,

Koutti³

et al. 2016

ijacsa

View full text Add to dashboard Cite

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.

show abstract

Section: B Vehicle Trackingmentioning

confidence: 99%

“…We begin by describing the association algorithm used to find relationship between consecutive images. More details can be found in [43]. Let us consider two edge points P [43], [44].…”

Section: ) Edge Curves Matching Processmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Approach for On-road Vehicle Detection and Tracking

Jaafari¹,

Ansari²,

Koutti³

et al. 2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…The associate point of Q k can be found as a correspondent point P k-1 belonging to the curves C k-1 in the horizontal neighborhood of Q k in previous image I k-1 . (More details about association method are described in [20]). …”

Section: Associationmentioning

confidence: 99%

“…At each time, the decision of the presence of vehicles in the road scene is made based on the current frame and its preceding one. We use the association approach [20], which consists in finding the relationship between consecutive frames. This method exploits the displacement of edges in the frames.…”

Section: Introduction Detection Of Road Obstacles [1] [2] [3] [4] [5]mentioning

confidence: 99%

A new vehicle detection method

Zebbara¹,

Mazoul²,

Ansari³

2011

SpecialIssue

View full text Add to dashboard Cite

Abstract-This paper presents a new vehicle detection method from images acquired by cameras embedded in a moving vehicle. Given the sequence of images, the proposed algorithms should detect out all cars in realtime. Related to the driving direction, the cars can be classified into two types. Cars drive in the same direction as the intelligent vehicle (IV) and cars drive in the opposite direction. Due to the distinct features of these two types, we suggest to achieve this method in two main steps. The first one detects all obstacles from images using the so-called association combined with corner detector. The second step is applied to validate each vehicle using AdaBoost classifier. The new method has been applied to different images data and the experimental results validate the efficacy of our method.

show abstract

Automatic road sign detection and recognition based on neural network

2022

View full text Add to dashboard Cite

Road sign detection and recognition is an integral part of intelligent transportation systems (ITS). It increases protection by reminding the driver of the current condition of the route, such as notices, bans, limitations and other valuable driving information. This paper describes a novel system for automatic detection and recognition of road signs, which is achieved in two main steps. First, the initial image is pre-processed using DBSCAN clustering algorithm. The clustering is performed based on color information, and the generated clusters are segmented using Artificial neural networks (ANN) classifier. The resulting ROIs are then carried out based on their aspect ratio and size to retain only significant ones. Then, a shape-based classification is performed using ANN as classifier and HDSO as feature to detect the circular, rectangular and triangular shapes. Second, a hybrid feature is defined to recognize the ROIs detected from the first step. It involves a combination of the so-called GLBP-Color which is an extension of the classical gradient local binary patterns (GLPB) feature to the RGB color space and the local self-similarity (LSS) feature. ANN, Adaboost and support vector machine (SVM) have been tested with the introduced hybrid feature and the first one is selected as it outperforms the other two. The proposed method has been tested in outdoor scenes, using a collection of common databasets, well known in the traffic sign community (GTSRB, GTSDB and STS). The results demonstrate the effectiveness of our method when compared to recent state-of-the-art methods. KeywordsTraffic sign detection • Traffic sign recognition • Color segmentation • Artificial neural networks (ANN) • Support vector machines (SVMs) • Histogram of dominant silhouette orientation • Gradient local binary patterns (GLBP) • Local self-similarity (LSS).

show abstract

Temporal consistent real-time stereo for intelligent vehicles

Cited by 26 publications

References 35 publications

A Novel Approach for On-road Vehicle Detection and Tracking

A Novel Approach for On-road Vehicle Detection and Tracking

A new vehicle detection method

Automatic road sign detection and recognition based on neural network

Contact Info

Product

Resources

About