scholarly journals A Comparison of Bottom-Up Models for Spatial Saliency Predictions in Autonomous Driving

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6825
Author(s):  
Jaime Maldonado ◽  
Lino Antoni Giefer
Keyword(s):  
Autonomous Driving ◽  
Systematic Evaluation ◽  
Highway Traffic ◽  
Bottom Up ◽  
Distinctive Features ◽  
Detection And Tracking ◽  
Saliency Maps ◽  
Salient Regions ◽  
Shape Complexity ◽  
Object Level

Bottom-up saliency models identify the salient regions of an image based on features such as color, intensity and orientation. These models are typically used as predictors of human visual behavior and for computer vision tasks. In this paper, we conduct a systematic evaluation of the saliency maps computed with four selected bottom-up models on images of urban and highway traffic scenes. Saliency both over whole images and on object level is investigated and elaborated in terms of the energy and the entropy of the saliency maps. We identify significant differences with respect to the amount, size and shape-complexity of the salient areas computed by different models. Based on these findings, we analyze the likelihood that object instances fall within the salient areas of an image and investigate the agreement between the segments of traffic participants and the saliency maps of the different models. The overall and object-level analysis provides insights on the distinctive features of salient areas identified by different models, which can be used as selection criteria for prospective applications in autonomous driving such as object detection and tracking.

scholarly journals Eight-Channel Multispectral Image Database for Saliency Prediction

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 970
Author(s):  
Miguel Ángel Martínez-Domingo ◽  
Juan Luis Nieves ◽  
Eva M. Valero
Keyword(s):  
Imaging Techniques ◽  
Image Database ◽  
Multispectral Image ◽  
Urban Scenes ◽  
Saliency Maps ◽  
Saliency Prediction ◽  
New Models ◽  
Rgb Images ◽  
Salient Regions ◽  
Rgb Image

Saliency prediction is a very important and challenging task within the computer vision community. Many models exist that try to predict the salient regions on a scene from its RGB image values. Several new models are developed, and spectral imaging techniques may potentially overcome the limitations found when using RGB images. However, the experimental study of such models based on spectral images is difficult because of the lack of available data to work with. This article presents the first eight-channel multispectral image database of outdoor urban scenes together with their gaze data recorded using an eyetracker over several observers performing different visualization tasks. Besides, the information from this database is used to study whether the complexity of the images has an impact on the saliency maps retrieved from the observers. Results show that more complex images do not correlate with higher differences in the saliency maps obtained.

A Unified Approach of Object-Level Saliency Detection in Images and Videos

2013 ◽  
Vol 765-767 ◽  
pp. 1401-1405
Author(s):  
Chi Zhang ◽  
Wei Qiang Wang
Keyword(s):  
Quality Evaluation ◽  
Saliency Detection ◽  
Visual Saliency ◽  
Unified Approach ◽  
Feature Maps ◽  
Still Images ◽  
Saliency Maps ◽  
Novel Method ◽  
Object Level ◽  
Important Branch

Object-level saliency detection is an important branch of visual saliency. In this paper, we propose a novel method which can conduct object-level saliency detection in both images and videos in a unified way. We employ a more effective spatial compactness assumption to measure saliency instead of the popular contrast assumption. In addition, we present a combination framework which integrates multiple saliency maps generated in different feature maps. The proposed algorithm can automatically select saliency maps of high quality according to the quality evaluation score we define. The experimental results demonstrate that the proposed method outperforms all state-of-the-art methods on both of the datasets of still images and video sequences.

scholarly journals Image Saliency Prediction in Transformed Domain: A Deep Complex Neural Network Method

2019 ◽  
Vol 33 ◽  
pp. 8521-8528 ◽  
Author(s):  
Lai Jiang ◽  
Zhe Wang ◽  
Mai Xu ◽  
Zulin Wang
Keyword(s):  
Neural Network ◽  
Amplitude Spectrum ◽  
Ground Truth ◽  
Phase Spectrum ◽  
Saliency Maps ◽  
Image Saliency ◽  
Saliency Prediction ◽  
Network Method ◽  
Salient Regions ◽  
Robust To Noise

The transformed domain fearures of images show effectiveness in distinguishing salient and non-salient regions. In this paper, we propose a novel deep complex neural network, named SalDCNN, to predict image saliency by learning features in both pixel and transformed domains. Before proposing Sal-DCNN, we analyze the saliency cues encoded in discrete Fourier transform (DFT) domain. Consequently, we have the following findings: 1) the phase spectrum encodes most saliency cues; 2) a certain pattern of the amplitude spectrum is important for saliency prediction; 3) the transformed domain spectrum is robust to noise and down-sampling for saliency prediction. According to these findings, we develop the structure of SalDCNN, including two main stages: the complex dense encoder and three-stream multi-domain decoder. Given the new SalDCNN structure, the saliency maps can be predicted under the supervision of ground-truth fixation maps in both pixel and transformed domains. Finally, the experimental results show that our Sal-DCNN method outperforms other 8 state-of-theart methods for image saliency prediction on 3 databases.

scholarly journals Lidar Data Analysis for Time to Headway Determination in the DriveSafe Project Field Tests

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
İlker Altay ◽  
Bilin Aksun Güvenç ◽  
Levent Güvenç
Keyword(s):  
Field Tests ◽  
Simple Algorithm ◽  
Forward Direction ◽  
Tracking Algorithm ◽  
Lidar Data ◽  
Highway Traffic ◽  
Huge Amount ◽  
Detection And Tracking ◽  
Straight Line ◽  
Point Distance

The DriveSafe project was carried out by a consortium of university research centers and automotive OEMs in Turkey to reduce accidents caused by driver behavior. A huge amount of driving data was collected from 108 drivers who drove the instrumented DriveSafe vehicle in the same route of 25 km of urban and highway traffic in Istanbul. One of the sensors used in the DriveSafe vehicle was a forward-looking LIDAR. The data from the LIDAR is used here to determine and record the headway time characteristics of different drivers. This paper concentrates on the analysis of LIDAR data from the DriveSafe vehicle. A simple algorithm that only looks at the forward direction along a straight line is used first. Headway times based on this simple approach are presented for an example driver. A more accurate detection and tracking algorithm taken from the literature are presented later in the paper. Grid-based and point distance-based methods are presented first. Then, a detection and tracking algorithm based on the Kalman filter is presented. The results are demonstrated using experimental data.

scholarly journals Systematic Correlation Matrix Evaluation (SCoMaE) – A bottom-up, natural science-based approach to identify Indicators

2017 ◽  
Author(s):  
Nadine Mengis ◽  
David P. Keller ◽  
Andreas Oschlies
Keyword(s):  
Climate Change ◽  
Correlation Matrix ◽  
Expert Judgment ◽  
Comparative Assessment ◽  
Systematic Evaluation ◽  
Climate Change Scenarios ◽  
Scenario Simulation ◽  
Earth System ◽  
Bottom Up ◽  
The Earth

Abstract. This study introduces the Systematic Correlation Matrix Evaluation (SCoMaE) method, a bottom-up approach which combines expert judgment and statistical information to systematically select transparent, non redundant indicators for a com- prehensive assessment of the state of the Earth system. The methods consists of three basic steps: 1) Calculation of a correlation matrix among variables relevant for a given research question, 2) Systematic evaluation of the matrix, to identify clusters of variables with similar behavior and respective mutually independent indicators, and 3) Interpretation of the identified clusters, enabling a learning effect from the selection of indicators. Optional further analysis steps include: 4) Testing the robustness of identified clusters with respect to changes in forcing or boundary conditions, 5) Enabling a comparative assessment of varying scenarios by constructing and evaluating a common correlation matrix, or 6) Inclusion of expert judgment such as to prescribe indicators, to allow for considerations other than statistical consistency. The exemplary application of the SCoMaE method to Earth system model output forced by different CO2 emission scenarios reveals the necessity of re-evaluating indicators identified in a historical scenario simulation for an accurate assessment of an intermediate-high, as well as a business-as-usual, climate change scenario simulation, which arises from changes in prevailing correlations in the Earth system under varying climate forcing. For a comparative assessment of the three climate change scenarios, we construct and evaluate a common correlation matrix, in which we identify robust correlations between variables across the three considered scenarios.

scholarly journals Multiple Road-Objects Detection and Tracking for Autonomous Driving

2021 ◽  
Author(s):  
Wael Farag ◽  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
High Performance ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Autonomous Driving ◽  
Estimation Accuracy ◽  
Time Performance ◽  
Detection And Tracking ◽  
Objects Detection

In this paper, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. The UKF has outperformed the EKF on all test cases and all the state variable levels (-24% average RMSE). The employed fusion technique show how outstanding is the improvement in tracking performance compared to the use of a single device (-29% RMES with lidar and -38% RMSE with radar).

scholarly journals Train here, drive there: ROS based end-to-end autonomous-driving pipeline validation in CARLA simulator using the NHTSA typology

2021 ◽  
Author(s):  
Carlos Gómez-Huélamo ◽  
Javier Del Egido ◽  
Luis M. Bergasa ◽  
Rafael Barea ◽  
Elena López-Guillén ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Ad Hoc ◽  
Autonomous Driving ◽  
Use Cases ◽  
Highway Traffic ◽  
Cruise Control ◽  
National Highway Traffic Safety ◽  
Urban Complex ◽  
Pedestrian Crossing ◽  
The Cost

AbstractUrban complex scenarios are the most challenging situations in the field of Autonomous Driving (AD). In that sense, an AD pipeline should be tested in countless environments and scenarios, escalating the cost and development time exponentially with a physical approach. In this paper we present a validation of our fully-autonomous driving architecture using the NHTSA (National Highway Traffic Safety Administration) protocol in the CARLA simulator, focusing on the analysis of our decision-making module, based on Hierarchical Interpreted Binary Petri Nets (HIBPN). First, the paper states the importance of using hyper-realistic simulators, as a preliminary help to real test, as well as an appropriate design of the traffic scenarios as the two current keys to build safe and robust AD technology. Second, our pipeline is introduced, which exploits the concepts of standard communication in robotics using the Robot Operating System (ROS) and the Docker approach to provide the system with isolation, flexibility and portability, describing the main modules and approaches to perform the navigation. Third, the CARLA simulator is described, outlining the steps carried out to merge our architecture with the simulator and the advantages to create ad-hoc driving scenarios for use cases validation instead of just modular evaluation. Finally, the architecture is validated using some challenging driving scenarios such as Pedestrian Crossing, Stop, Adaptive Cruise Control (ACC) and Unexpected Pedestrian. Some qualitative (video files: Simulation Use Cases) and quantitative (linear velocity and trajectory splitted in the corresponding HIBPN states) results are presented for each use case, as well as an analysis of the temporal graphs associated to the Vulnerable Road Users (VRU) cases, validating our architecture in simulation as a preliminary stage before implementing it in our real autonomous electric car.

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