A Real-Time Multi-scale Vehicle Detection and Tracking Approach for Smartphones

Romera, Eduardo; Bergasa, Luis M.; Arroyo, Roberto

doi:10.1109/itsc.2015.213

Cited by 23 publications

(20 citation statements)

References 16 publications

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“…Additionally, the unified knowledge about the whole road environment allows for better application of inter-class relations to improve tracking. For instance, some vehicle detectors like [11] rely on lane detection to track according to geometrical constraints and to filter impossible vehicle locations. By having full segmentation, these relations are easier to apply (see Fig.…”

Section: Discussion On General Concernsmentioning

confidence: 99%

Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?

Romera,

Bergasa,

Arroyo

2016

Preprint

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Autonomous driving is a challenging topic that requires complex solutions in perception tasks such as recognition of road, lanes, traffic signs or lights, vehicles and pedestrians. Through years of research, computer vision has grown capable of tackling these tasks with monocular detectors that can provide remarkable detection rates with relatively low processing times. However, the recent appearance of Convolutional Neural Networks (CNNs) has revolutionized the computer vision field and has made possible approaches to perform full pixel-wise semantic segmentation in times close to real time (even on hardware that can be carried on a vehicle). In this paper, we propose to use full image segmentation as an approach to simplify and unify most of the detection tasks required in the perception module of an autonomous vehicle, analyzing major concerns such as computation time and detection performance.

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Section: Discussion On General Concernsmentioning

confidence: 99%

Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?

Romera,

Bergasa,

Arroyo

2016

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are two more coefficients that account for tangential distortion: p 1 and p 2 , and this distortion can be corrected using a different correction formula as given by Eq. ( 21) and (22).…”

Section: Camera Calibrationmentioning

confidence: 99%

“…In [22], Romera et al proposed a lightweight technique for vehicle detection and tracking that is implemented on a smartphone. The technique detects lanes first and determines the vanishing points based on previous work [23].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Vehicle-Detection-and-Tracking Technique for Autonomous Driving

Wael¹

2020

IJCDS

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In this paper, an advanced-and-reliable vehicle detection-and-tracking technique is proposed and implemented. The Real-Time Vehicle Detection-and-Tracking (RT_VDT) technique is well suited for Advanced Driving Assistance Systems (ADAS) applications or Self-Driving Cars (SDC). The RT_VDT is mainly a pipeline of reliable computer vision and machine learning algorithms that augment each other and take in raw RGB images to produce the required boundary boxes of the vehicles that appear in the front driving space of the car. The main contribution of this paper is the precise fusion of the employed algorithms where some of them work in parallel to strengthen each other in order to produce a precise and sophisticated real-time output. In addition, the RT_VDT provides fast enough computation to be embedded in CPUs that are currently employed by ADAS systems. Each used algorithm is described in detail, implemented, its performance is evaluated using actual road images, and videos captured by the front-mounted camera of the car. The evaluation of the RT_VDT shows that it reliably detects and tracks vehicle boundaries under various conditions.

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“…Additionally, some scholars studied driving behavior using mobile-based signals, for example, ref. [35][36][37]. However, the majority research above relied on image data rather than focused on the deep end-to-end network based on the data from on-board sensors for driving behavior prediction.…”

Section: Introductionmentioning

confidence: 99%

Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network

Yang

Zhao²,

Lü

et al. 2021

Sensors

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Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.

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A Real-Time Multi-scale Vehicle Detection and Tracking Approach for Smartphones

Cited by 23 publications

References 16 publications

Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?

Can we unify monocular detectors for autonomous driving by using the pixel-wise semantic segmentation of CNNs?

A Comprehensive Vehicle-Detection-and-Tracking Technique for Autonomous Driving

Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network

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