scholarly journals Real-Time Vehicle Detection Using Cross-Correlation and 2D-DWT for Feature Extraction

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Abdelmoghit Zaarane ◽  
Ibtissam Slimani ◽  
Abdellatif Hamdoun ◽  
Issam Atouf
Keyword(s):  
Real Time ◽  
Template Matching ◽  
Cross Correlation ◽  
Vehicle Detection ◽  
Discrete Wavelet ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Step Potential ◽  
Assistance Systems

Nowadays, real-time vehicle detection is one of the biggest challenges in driver-assistance systems due to the complex environment and the diverse types of vehicles. Vehicle detection can be exploited to accomplish several tasks such as computing the distances to other vehicles, which can help the driver by warning to slow down the vehicle to avoid collisions. In this paper, we propose an efficient real-time vehicle detection method following two steps: hypothesis generation and hypothesis verification. In the first step, potential vehicles locations are detected based on template matching technique using cross-correlation which is one of the fast algorithms. In the second step, two-dimensional discrete wavelet transform (2D-DWT) is used to extract features from the hypotheses generated in the first step and then to classify them as vehicles and nonvehicles. The choice of the classifier is very important due to the pivotal role that plays in the quality of the final results. Therefore, SVMs and AdaBoost are two classifiers chosen to be used in this paper and their results are compared thereafter. The results of the experiments are compared with some existing system, and it showed that our proposed system has good performance in terms of robustness and accuracy and that our system can meet the requirements in real time.

scholarly journals Convolution Neural Network with Selective Multi-Stage Feature Fusion: Case Study on Vehicle Rear Detection

2018 ◽  
Vol 8 (12) ◽  
pp. 2468 ◽  
Author(s):  
Won-Jae Lee ◽  
Dong Kim ◽  
Tae-Koo Kang ◽  
Myo-Taeg Lim
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Vehicle Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Adaboost Algorithm ◽  
Multi Stage ◽  
Assistance Systems ◽  
Visualization Techniques

Vision-based vehicle detection is the most basic and important technology in advanced driver assistance systems. In this paper, we propose a vehicle detection framework using selective multi-stage features in convolutional neural networks (CNNs) to improve vehicle detection performance. A 10-layer CNN model was designed and visualization techniques were used to selectively extract features from the activation feature map, called selective multi-stage features. The proposed features contain characteristic vehicle image information and are more robust than traditional features against noise. We trained the AdaBoost algorithm using these features to implement a vehicle detector. The experimental results verified that the proposed vehicle detection framework exhibited better performance than previous frameworks.

scholarly journals AFFECTIVE-SOUND EFFECTS ON DRIVING BEHAVIOUR

Transport ◽  
2013 ◽  
Vol 29 (1) ◽  
pp. 100-106 ◽  
Author(s):  
Jesús Serrano ◽  
Leandro Luigi Di Stasi ◽  
Alberto Megías ◽  
Andrés Catena
Keyword(s):  
Driving Safety ◽  
Auditory Signal ◽  
Road Accidents ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Emotional Quality ◽  
Sound Effects ◽  
Technological Developments ◽  
Assistance Systems

Recent technological developments in active advanced driver assistance systems and in-car infotainment devices have contributed to reducing the number and severity of road accidents as well as improving and simplifying driver experience. However, these systems may impact driving performance in undesired ways, especially when emotionally-charged stimuli are used as warning signals. Emotional distraction can be a serious danger, causing delays in information processing, and reducing driving safety below minimal acceptable levels. Here we study the effect of emotionally-laden auditory signals on the speed of concurrent driving decisions. We distinguished two categories of behavioural responses: ‘urgent’ vs ‘evaluative’. In the experiments reported here participants were quicker to evaluate whether a traffic scene was risky or not after hearing an emotionally-charged auditory stimulus than after a neutral one. However, urgent (braking) responses to the same scenes were not affected by the emotional quality of the auditory signal. Based on these results, we give preliminary advice on the design of guidelines for in-car interfaces particularly in the field of affective in-car computing.

scholarly journals Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3217 ◽  
Author(s):  
Jaechan Cho ◽  
Yongchul Jung ◽  
Dong-Sun Kim ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Moving Object Detection ◽  
Moving Object ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Moving Camera ◽  
Real Time Processing ◽  
Time Processing ◽  
Assistance Systems

Most approaches for moving object detection (MOD) based on computer vision are limited to stationary camera environments. In advanced driver assistance systems (ADAS), however, ego-motion is added to image frames owing to the use of a moving camera. This results in mixed motion in the image frames and makes it difficult to classify target objects and background. In this paper, we propose an efficient MOD algorithm that can cope with moving camera environments. In addition, we present a hardware design and implementation results for the real-time processing of the proposed algorithm. The proposed moving object detector was designed using hardware description language (HDL) and its real-time performance was evaluated using an FPGA based test system. Experimental results demonstrate that our design achieves better detection performance than existing MOD systems. The proposed moving object detector was implemented with 13.2K logic slices, 104 DSP48s, and 163 BRAM and can support real-time processing of 30 fps at an operating frequency of 200 MHz.

scholarly journals How Accurate Can UWB and Dead Reckoning Positioning Systems Be? Comparison to SLAM Using the RPLidar System

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3761
Author(s):  
Damian Grzechca ◽  
Adam Ziębiński ◽  
Krzysztof Paszek ◽  
Krzysztof Hanzel ◽  
Adam Giel ◽  
...  
Keyword(s):  
Dead Reckoning ◽  
Ultra Wideband ◽  
Commercial Vehicles ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
New Approach ◽  
Positioning Systems ◽  
Localization And Mapping ◽  
Assistance Systems

This paper compares two positioning systems, namely ultra-wideband (UWB) based micro-location technology and dead reckoning and a RPLidar based simultaneous localization and mapping (SLAM) solution. This new approach can be used to improve the quality of the positioning system and increase the functionality of advanced driver assistance systems (ADAS). This is achieved by using stationary nodes and UWB tags on the vehicles. Thus, the redundancy of localization can be achieved by this approach, e.g., as a backup to onboard sensors like RPlidar or radar. Additionally, UWB based micro-location allows additional data channels to be used for communication purposes. Furthermore, it is shown that the regular use of correction data increases UWB and dead reckoning accuracy. These correction data can be based on onboard sensors. This shows that it is promising to develop a system that fuses onboard sensors and micro-localization for safety-critical tasks like the platooning of commercial vehicles.

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