Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment

Li, Fuliang; Zhang, Ronghui; Feng, Yuanjing

doi:10.1049/iet-ipr.2016.0931

Cited by 27 publications

(15 citation statements)

References 28 publications

(27 reference statements)

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“…Merdrignac et al [95] use the complementary features of perception and communication to solve the safety problems of vulnerable road users via the method of perception and communication fusion. Li et al [96] applied vision and communication technology for pedestrian detection training. A cooperative early warning system based on smartphones is developed to achieve blind spot detection sharing.…”

Section: Research On Traffic Conflict Based On Intelligent Vehiclmentioning

confidence: 99%

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Huang

et al. 2020

IEEE Access

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The traffic situation at an urban intersection is complicated, due to the numerous internal conflicts and all kinds of traffic accidents. With the objective of improving road safety, the automotive industry is moving toward intelligent vehicles. The major challenges are accurately perceiving the road traffic environment, detecting the potential traffic conflicts, and proposing the alternative driving strategies. This paper summarizes the existing researches on traffic conflicts from the perspective of intelligent vehicles. The intelligent vehicles can perceive the surrounding environment, extract road condition information, and detect obstacles for avoiding collisions or mitigating accidents. It expounds that the perception technology of intelligent vehicles can be divided into three main categories, namely, the perception technology, the communication technology, and the fusion of perception-communication technology. At the same time, the existing technical problems are analyzed. Finally, future development trends in this field are discussed. INDEX TERMS Intelligent vehicles, traffic conflicts, V2X communications, environment perception. I. INTRODUCTION

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Section: Research On Traffic Conflict Based On Intelligent Vehiclmentioning

confidence: 99%

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Huang

et al. 2020

IEEE Access

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“…The face detection was conducted using Continuously Adaptive Meanshift (CAMShift), an algorithm used to determine objects in a video by applying meanshift in each frame. The CAMShift algorithm is available in MATLAB Library and has been widely used in various types of research due to its ease of applying algorithms [17]. Some studies use it for learning local features of face recognition online [18], hand gesture recognition [19], and player tracking system [20].…”

Section: Face Detectionmentioning

confidence: 99%

Combination of fast hybrid classification and k value optimization in k-nn for video face recognition

Septiana

Suciati

2020

register. jurnal. ilm. teknologi. sistem. inf.

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Nowadays, the need for face recognition is no longer include images only but also videos. However, there are some challenges associated with the addition of this new technique such as how to determine the right pre-processing, feature extraction, and classification methods to obtain excellent performance. Although nowadays the k-Nearest Neighbor (k-NN) is widely used, high computational costs due to numerous features of the dataset and large amount of training data makes adequate processing difficult. Several studies have been conducted to improve the performance of k-NN using the FHC (Fast Hybrid Classification) method by optimizing the local k values. One of the disadvantages of the FHC Method is that the k value used is still in the default form. Therefore, this research proposes the use of k-NN value optimization methods in FHC, thereby, increasing its accuracy. The Fast Hybrid Classification which combines the k-means clustering with k-NN, groups the training data into several prototypes called TLDS (Two Level Data Structure). Furthermore, two classification levels are applied to label test data, with the first used to determine the n number of prototypes with the same class in the test data. The second classification using the optimized k value in the k-NN method, is employed to sharpen the accuracy, when the same number of prototypes does not reach n. The evaluation results show that this method provides 86% accuracy and time performance of 3.3 seconds.

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“…For vehicle feature representation, the most common features are Histogram Orientation Gradients (HOG) features designed by Dalal [7], Haar features designed by Papageorgiou [8], and LBP features [9]. Some deformation features based on these three kinds of features are also used in vehicle detection applications.…”

Section: Related Workmentioning

confidence: 99%

A Vehicle Recognition Algorithm Based on Deep Transfer Learning with a Multiple Feature Subspace Distribution

Wang

Cai

et al. 2018

Sensors

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Vehicle detection is a key component of environmental sensing systems for Intelligent Vehicles (IVs). The traditional shallow model and offline learning-based vehicle detection method are not able to satisfy the real-world challenges of environmental complexity and scene dynamics. Focusing on these problems, this work proposes a vehicle detection algorithm based on a multiple feature subspace distribution deep model with online transfer learning. Based on the multiple feature subspace distribution hypothesis, a deep model is established in which multiple Restricted Boltzmann Machines (RBMs) construct the lower layers and a Deep Belief Network (DBN) composes the superstructure. For this deep model, an unsupervised feature extraction method is applied, which is based on sparse constraints. Then, a transfer learning method with online sample generation is proposed based on the deep model. Finally, the entire classifier is retrained online with supervised learning. The experiment is actuated using the KITTI road image datasets. The performance of the proposed method is compared with many state-of-the-art methods and it is demonstrated that the proposed deep transfer learning-based algorithm outperformed existing state-of-the-art methods.

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Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment

Cited by 27 publications

References 28 publications

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

A Review of Research on Traffic Conflicts Based on Intelligent Vehicles

Combination of fast hybrid classification and k value optimization in k-nn for video face recognition

A Vehicle Recognition Algorithm Based on Deep Transfer Learning with a Multiple Feature Subspace Distribution

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