Pedestrian detection in far-infrared daytime images using a hierarchical codebook of SURF

One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Motorcycles that see: Multifocal stereo vision sensor for advanced safety systems in tilting vehicles

Advanced driver assistance systems, ADAS, have shown the possibility to anticipate crash accidents and effectively assist road users in critical traffic situations. This is not the case for motorcyclists, in fact ADAS for motorcycles are still barely developed. Our aim was to study a camera-based sensor for the application of preventive safety in tilting vehicles. We identified two road conflict situations for which automotive remote sensors installed in a tilting vehicle are likely to fail in the identification of critical obstacles. Accordingly, we set two experiments conducted in real traffic conditions to test our stereo vision sensor. Our promising results support the application of this type of sensors for advanced motorcycle safety applications

Real-time object detection using monocular vision for low-cost automotive sensing systems

This work addresses the problem of real-time object detection in automotive environments using monocular vision. The focus is on real-time feature detection, tracking, depth estimation using monocular vision and finally, object detection by fusing visual saliency and depth information. Firstly, a novel feature detection approach is proposed for extracting stable and dense features even in images with very low signal-to-noise ratio. This methodology is based on image gradients, which are redefined to take account of noise as part of their mathematical model. Each gradient is based on a vector connecting a negative to a positive intensity centroid, where both centroids are symmetric about the centre of the area for which the gradient is calculated. Multiple gradient vectors define a feature with its strength being proportional to the underlying gradient vector magnitude. The evaluation of the Dense Gradient Features (DeGraF) shows superior performance over other contemporary detectors in terms of keypoint density, tracking accuracy, illumination invariance, rotation invariance, noise resistance and detection time. The DeGraF features form the basis for two new approaches that perform dense 3D reconstruction from a single vehicle-mounted camera. The first approach tracks DeGraF features in real-time while performing image stabilisation with minimal computational cost. This means that despite camera vibration the algorithm can accurately predict the real-world coordinates of each image pixel in real-time by comparing each motion-vector to the ego-motion vector of the vehicle. The performance of this approach has been compared to different 3D reconstruction methods in order to determine their accuracy, depth-map density, noise-resistance and computational complexity. The second approach proposes the use of local frequency analysis of i ii gradient features for estimating relative depth. This novel method is based on the fact that DeGraF gradients can accurately measure local image variance with subpixel accuracy. It is shown that the local frequency by which the centroid oscillates around the gradient window centre is proportional to the depth of each gradient centroid in the real world. The lower computational complexity of this methodology comes at the expense of depth map accuracy as the camera velocity increases, but it is at least five times faster than the other evaluated approaches. This work also proposes a novel technique for deriving visual saliency maps by using Division of Gaussians (DIVoG). In this context, saliency maps express the difference of each image pixel is to its surrounding pixels across multiple pyramid levels. This approach is shown to be both fast and accurate when evaluated against other state-of-the-art approaches. Subsequently, the saliency information is combined with depth information to identify salient regions close to the host vehicle. The fused map allows faster detection of high-risk areas where obstacles are likely to exist. As a result, existing object detection algorithms, such as the Histogram of Oriented Gradients (HOG) can execute at least five times faster. In conclusion, through a step-wise approach computationally-expensive algorithms have been optimised or replaced by novel methodologies to produce a fast object detection system that is aligned to the requirements of the automotive domain

A Review of Sensor Technologies for Perception in Automated Driving

After more than 20 years of research, ADAS are common in modern vehicles available in the market. Automated Driving systems, still in research phase and limited in their capabilities, are starting early commercial tests in public roads. These systems rely on the information provided by on-board sensors, which allow to describe the state of the vehicle, its environment and other actors. Selection and arrangement of sensors represent a key factor in the design of the system. This survey reviews existing, novel and upcoming sensor technologies, applied to common perception tasks for ADAS and Automated Driving. They are put in context making a historical review of the most relevant demonstrations on Automated Driving, focused on their sensing setup. Finally, the article presents a snapshot of the future challenges for sensing technologies and perception, finishing with an overview of the commercial initiatives and manufacturers alliances that will show future market trends in sensors technologies for Automated Vehicles.This work has been partly supported by ECSEL Project ENABLE- S3 (with grant agreement number 692455-2), by the Spanish Government through CICYT projects (TRA2015- 63708-R and TRA2016-78886-C3-1-R)