Juan Diego Ortega

Vicomtech · Intelligent Transport Systems and Engineering Department

Ensuring the safety of water airport runways is essential for the correct operation of seaplane flights. Among other tasks, airport operators must identify and remove various objects that may have drifted into the runway area. In this paper, the authors propose a complete and embedded‐friendly waterway obstacle detection pipeline that runs on a cam...

This paper concerns a methodology of a semi-automatic annotation strategy for the gaze estimation material of the Driver Monitoring Dataset (DMD). It consists of a pipeline of semi-automatic annotation that uses ideas from Active Learning to annotate data with an accuracy as high as possible using less human intervention. A dummy model (the initial...

Strategies that include the generation of synthetic data are beginning to be viable as obtaining real data can be logistically complicated, very expensive or slow. Not only the capture of the data can lead to complications, but also its annotation. To achieve high-fidelity data for training intelligent systems, we have built a 3D scenario and set-u...

Tremendous advances in advanced driver assistance systems (ADAS) have been possible thanks to the emergence of deep neural networks (DNN) and Big Data (BD) technologies. Huge volumes of data can be managed and consumed as training material to create DNN models which feed functions such as lane keeping systems (LKS), automated emergency braking (AEB...

Driver Monitoring Systems (DMS) operate by measuring the state of the driver while performing driving activities. At the gates of the arrival of SAE-L3 autonomous driving vehicles, DMS are called to play a major role for guarantee or, at least, support safer mode transfer transitions (between manual and automated driving modes). Drowsiness and fati...

The recently presented Driver Monitoring Dataset (DMD) extends research lines for Driver Monitoring Systems. We intend to explore this dataset and apply commonly used methods for action recognition to this specific context, from image-based to video-based analysis. Specially, we aim to detect driver distraction by applying action recognition techni...

Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development, crucial for the transition of automated driving from SAE Level-2 to...

Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development, crucial for the transition of automated driving from SAE Level-2 to...

This paper presents a structured approach for efficiently exploiting the perspective information of a scene to enhance the detection of objects in monocular systems. It defines a finite grid of 3D positions on the dominant ground plane and computes occupancy maps from which object location estimates are extracted . This method works on the top of a...

This work elaborates on the cycles involved in developing ADAS applications which involve resources not permanently available in the vehicles. For example, data is collected from LIDAR, video cameras, precise localization, and user interaction with the ADAS features. These data are consumed by machine learning algorithms hosted locally or in the cl...

In this study, the authors analyse the exponential growth of advanced driver assistance systems based on video processing in the past decade. Specifically, they focus on how research and innovative ideas can finally reach the market as cost-effective solutions. They explore well-known computer vision methods for services like lane departure warning...

In this paper we describe a real-time approach for person detection in video footage, joint with a privacy masking tool, in the framework of forensic applications in CCTV systems. Particularly, this paper summarizes our results in these domains within the European FP7 SAVASA and P-REACT projects. Our main contributions have been focused on real-tim...

Embedding computer vision SW for in-vehicle applications requires the optimization of algorithms to fit into low cost and low consumption HW. Such optimization is a task substantially centered in improving the efficiency of the implementations, typically focused on the migration of algorithms to massive parallelization HW. The development cost asso...

The efficient detection and tracking of persons in videos has widrespread applications, specially in CCTV systems for surveillance or forensics applications. In this paper we present a new method for people detection and tracking based on the knowledge of the perspective information of the scene. It allows alleviating two main drawbacks of existing...

In this paper we analyse the recent exponential growth of applications based on video processing in the framework of Advanced Driver Assistance Systems (ADAS). Specifically, we focus on how such cost-effective solutions can finally reach the market starting from research and innovative ideas. We explore well known computer vision methods for servic...

This paper presents a single camera vehicle detection technique for forward collision warning systems suitable to be integrated in embedded platforms. It combines the robustness of detectors based on classification methods with an innovative perspective multi-scale procedure to scan the images that dramatically reduces the computational cost associ...

In this paper we analyze the recent exponential growth of applications based on video processing in the framework of Advanced Driver Assistance Systems (ADAS). Specifically, we focus on the cost-effective solutions provided by computer vision methods for services like lane departure warning systems, collision avoidance systems, etc. Along the paper...

In this paper we describe our participation in the semantic indexing (SIN) and interactive surveillance event detection (SED) tasks at TRECVid 2013 [11]. Our work was motivated by the goals of the EU SAVASA project (Standards-based Approach to Video Archive Search and Analysis) which supports search over multiple video archives. Our aims were: to a...