A Review on Video Based Vehicle Detection, Recognition and Tracking

S, Shobha B; Deepu, R

doi:10.1109/csitss.2018.8768743

Cited by 31 publications

(13 citation statements)

References 17 publications

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“…For detailed literature reviews on traditional vehicle detection, tracking and behaviour prediction algorithms please refer to [11][12][13][14][15]21,81]. Traditional vehicle detection is mainly performed by motion-based and appearance-based approaches.…”

Section: Traditional Techniquesmentioning

confidence: 99%

“…Sivaraman and Trivedi [11], Mukhtar et al [12], Abdulrahim and Salam [13], Antony and Suchetha [14], Shobha and Deepu [15], Abbas et al [16] performed reviews on vehicle detection algorithms; however, they only explored traditional techniques. There are limited papers that reviews DL vehicle detection algorithms, for example, Manana et al [17] reviewed vehicle detection system that uses DL technique; however, they covered systems that use satellites images and images that are acquired from a mono camera mounted at the back of the vehicle.…”

Section: Introductionmentioning

confidence: 99%

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Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Galvao

Abbod

Kalganova

et al. 2021

Sensors

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Autonomous Vehicles (AVs) have the potential to solve many traffic problems, such as accidents, congestion and pollution. However, there are still challenges to overcome, for instance, AVs need to accurately perceive their environment to safely navigate in busy urban scenarios. The aim of this paper is to review recent articles on computer vision techniques that can be used to build an AV perception system. AV perception systems need to accurately detect non-static objects and predict their behaviour, as well as to detect static objects and recognise the information they are providing. This paper, in particular, focuses on the computer vision techniques used to detect pedestrians and vehicles. There have been many papers and reviews on pedestrians and vehicles detection so far. However, most of the past papers only reviewed pedestrian or vehicle detection separately. This review aims to present an overview of the AV systems in general, and then review and investigate several detection computer vision techniques for pedestrians and vehicles. The review concludes that both traditional and Deep Learning (DL) techniques have been used for pedestrian and vehicle detection; however, DL techniques have shown the best results. Although good detection results have been achieved for pedestrians and vehicles, the current algorithms still struggle to detect small, occluded, and truncated objects. In addition, there is limited research on how to improve detection performance in difficult light and weather conditions. Most of the algorithms have been tested on well-recognised datasets such as Caltech and KITTI; however, these datasets have their own limitations. Therefore, this paper recommends that future works should be implemented on more new challenging datasets, such as PIE and BDD100K.

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Section: Traditional Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Galvao

Abbod

Kalganova

et al. 2021

Sensors

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“…Appearance‐based methods can identify vehicles without motion information and can be used to correctly identify moving vehicles from the image sequences 64 . These methods can detect a wide range of vehicles depending on the features under consideration 65 . The wide variation in the appearance of vehicles, the high similarity between vehicles, the camera vision domain, and the variation in the illumination limit the performance of appearance‐based methods in real‐world applications.…”

Section: Our Proposed Frameworkmentioning

confidence: 99%

A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Hashemi

Emami

Sangar

2021

Int J Communication

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Summary The vehicle detection and classification (VDC) problem has received much attention recently due to the increased security threats and the need to develop intelligent transportation systems. A large number of approaches have been proposed for the VDC problem using neural networks. To determine how neural networks‐based approaches have developed for the VDC in recent years, this paper surveys the VDC approaches through a literature review with the range Jan. 2012 through Apr. 2021. To do this, we introduce a new comparison framework to classify and compare the VDC approaches. Our proposed framework is composed of nine comparison dimensions: input data type, vehicle type, scale, scope, dynamicity, vehicle detection method, vehicle classification method, application, and evaluation method. Next, using the proposed framework, we discuss the evolution of the VDC approaches and identify several open issues that have emerged in the field. This paper provides a guide for researchers to use or design robust VDC systems with proper characteristics based on their needs.

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“…The features are then classified by multi class SVM to the type of the vehicle. Author in [21] have presented a detailed review on vehicle detection, recognition and tracking. Multi view methods for vehicle detection are also discussed.…”

Section: Literature Surveymentioning

confidence: 99%

Traffic Adaptive Deep Learning based Fine Grained Vehicle Categorization in Cluttered Traffic Videos

S¹,

Deepu²

2021

IJACSA

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Smart traffic management is being proposed for better management of traffic infrastructure and regulate traffic in smart cities. With surge of traffic density in many cities, smart traffic management becomes utmost necessity. Vehicle categorization, traffic density estimation and vehicle tracking are some of the important functionalities in smart traffic management. Vehicles must be categorized based on multiple levels like type, speed, direction of travel and vehicle attributes like color etc. for efficient tracking and traffic density estimation. Vehicle categorization becomes very challenging due to occlusions, cluttered backgrounds and traffic density variations. In this work, a traffic adaptive multi-level vehicle categorization using deep learning is proposed. The solution is designed to solve the problems in vehicle categorization in terms of occlusions, cluttered backgrounds.

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A Review on Video Based Vehicle Detection, Recognition and Tracking

Cited by 31 publications

References 17 publications

Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

Pedestrian and Vehicle Detection in Autonomous Vehicle Perception Systems—A Review

A new comparison framework to survey neural networks‐based vehicle detection and classification approaches

Traffic Adaptive Deep Learning based Fine Grained Vehicle Categorization in Cluttered Traffic Videos

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