A survey of advances in vision-based vehicle re-identification

Khan, Sultan Daud; Ullah, Habib

doi:10.1016/j.cviu.2019.03.001

Cited by 126 publications

(63 citation statements)

References 95 publications

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“…Crowd behaviors have been significantly investigated in the literature including [15][16][17][18][19][20][21][22]. The importance of such investigations comes from the impact of such behavior on the people safety.…”

Section: Literature Reviewmentioning

confidence: 99%

The Internet of Things for Crowd Panic Detection

Ullah¹,

Altamimi²,

Ramadan³

2020

IJACSA

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Crowd behavior detection is important for the smart cities applications such as people gathering for different events. However, it is a challenging problem due to the internal states of the crowd itself and the surrounding environment. This paper proposes a novel crowd behavior detection framework based on a number of parameters. We first exploit a computer vision approach based on scale invariant feature transform (SIFT) to classify the crowd behavior either into panic or normalness. We then consider a number of other parameters from the surroundings namely crowd coherency, social interaction, motion information, randomness in crowd speed, internal chaos level, crowd condition, crowd temporal history, and crowd vibration status along with time stamp. Subsequently, these parameters are fed to deep learning model during training stage and the behavior of the crowd is detected during the testing stage. The experimental results show that proposed method renders significant performance in terms of crowd behavior detection.

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Section: Literature Reviewmentioning

confidence: 99%

The Internet of Things for Crowd Panic Detection

Ullah¹,

Altamimi²,

Ramadan³

2020

IJACSA

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“…The rich sensing information of the environment provided by the cameras can also be used to extend the functions of driver assistance systems. Some applications under investigation include traffic light detection [ 8 ], traffic sign recognition [ 9 ], vehicle identification [ 10 ], vehicle speed detection [ 11 ], pedestrian recognition [ 12 ], overtaking detection [ 13 ], and parking assistance [ 14 ], etc. Thus, despite the depth measurement precision under varying illumination conditions, it still has great potential to use image-based approaches for the vehicular system development.…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Lin

Dai

et al. 2020

Sensors

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One major concern in the development of intelligent vehicles is to improve the driving safety. It is also an essential issue for future autonomous driving and intelligent transportation. In this paper, we present a vision-based system for driving assistance. A front and a rear on-board camera are adopted for visual sensing and environment perception. The purpose is to avoid potential traffic accidents due to forward collision and vehicle overtaking, and assist the drivers or self-driving cars to perform safe lane change operations. The proposed techniques consist of lane change detection, forward collision warning, and overtaking vehicle identification. A new cumulative density function (CDF)-based symmetry verification method is proposed for the detection of front vehicles. The motion cue obtained from optical flow is used for overtaking detection. It is further combined with a convolutional neural network to remove repetitive patterns for more accurate overtaking vehicle identification. Our approach is able to adapt to a variety of highway and urban scenarios under different illumination conditions. The experiments and performance evaluation carried out on real scene images have demonstrated the effectiveness of the proposed techniques.

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“…It means that the motion information does not take into account individual moving or static objects [19][20 [21]. These methods therefore, often need various features including multi-resolution histograms [22][23], spatio-temporal cuboids [24] [25], appearance or motion descriptors [26] [27] and spatio-temporal cubes [28] [29].…”

Section: Introductionmentioning

confidence: 99%

Challenges of Deep Learning for Crowd Analytics

Siraj¹

2019

Preprint

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In high population cities, the gatherings of large crowds in public places and public areas accelerate or jeopardize people safety and transportation, which is a key challenge to the researchers. Although much research has been carried out on crowd analytics, many of existing methods are problem-specific, i.e., methods learned from a specific scene cannot be properly adopted to other videos. Therefore, this presents weakness and the discovery of these researches, since additional training samples have to be found from diverse videos. This paper will investigate diverse scene crowd analytics with traditional and deep learning models. We will also consider pros and cons of these approaches. However, once general deep methods are investigated from large datasets, they can be consider to investigate different crowd videos and images. Therefore, it would be able to cope with the problem including to not limited to crowd density estimation, crowd people counting, and crowd event recognition. Deep learning models and approaches are required to have large datasets for training and testing. Many datasets are collected taking into account many different and various problems related to building crowd datasets, including manual annotations and increasing diversity of videos and images. In this paper, we will also propose many models of deep neural networks and training approaches to learn the feature modeling for crowd analytics.

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A survey of advances in vision-based vehicle re-identification

Cited by 126 publications

References 95 publications

The Internet of Things for Crowd Panic Detection

The Internet of Things for Crowd Panic Detection

A Vision-Based Driver Assistance System with Forward Collision and Overtaking Detection

Challenges of Deep Learning for Crowd Analytics

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