DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis

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 SAE Level-3. In this paper, we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which includes real and simulated driving scenarios: distraction, gaze allocation, drowsiness, hands-wheel interaction and context data, in 41 hours of RGB, depth and IR videos from 3 cameras capturing face, body and hands of 37 drivers. A comparison with existing similar datasets is included, which shows the DMD is more extensive, diverse, and multi-purpose. The usage of the DMD is illustrated by extracting a subset of it, the dBehaviourMD dataset, containing 13 distraction activities, prepared to be used in DL training processes. Furthermore, we propose a robust and real-time driver behaviour recognition system targeting a real-world application that can run on cost-efficient CPU-only platforms, based on the dBehaviourMD. Its performance is evaluated with different types of fusion strategies, which all reach enhanced accuracy still providing real-time response.Comment: Accepted to ECCV 2020 workshop - Assistive Computer Vision and Robotic

DeepCut: Joint Subset Partition and Labeling for Multi Person Pose Estimation

This paper considers the task of articulated human pose estimation of multiple people in real world images. We propose an approach that jointly solves the tasks of detection and pose estimation: it infers the number of persons in a scene, identifies occluded body parts, and disambiguates body parts between people in close proximity of each other. This joint formulation is in contrast to previous strategies, that address the problem by first detecting people and subsequently estimating their body pose. We propose a partitioning and labeling formulation of a set of body-part hypotheses generated with CNN-based part detectors. Our formulation, an instance of an integer linear program, implicitly performs non-maximum suppression on the set of part candidates and groups them to form configurations of body parts respecting geometric and appearance constraints. Experiments on four different datasets demonstrate state-of-the-art results for both single person and multi person pose estimation. Models and code available at http://pose.mpi-inf.mpg.de.Comment: Accepted at IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2016

Deep Learning for Head Pose Estimation: A Survey

Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic

Automated Testing and Debugging for Big Data Analytics

The prevalence of big data analytics in almost every large-scale software system has generated a substantial push to build data-intensive scalable computing (DISC) frameworks such as Google MapReduce and Apache Spark that can fully harness the power of existing data centers. However, frameworks once used by domain experts are now being leveraged by data scientists, business analysts, and researchers. This shift in user demographics calls for immediate advancements in the development, debugging, and testing practices of big data applications, which are falling behind compared to the DISC framework design and implementation. In practice, big data applications often fail as users are unable to test all behaviors emerging from interleaving dataflow operators, user-defined functions, and framework's code. "Testing based on a random sample" rarely guarantees the reliability and "trial and error" and "print" debugging methods are expensive and time-consuming. Thus, the current practice of developing a big data application must be improved and the tools built to enhance the developer's productivity must adapt to the distinct characteristics of data-intensive scalable computing. By synthesizing ideas from software engineering and database systems, our hypothesis is that we can design effective and scalable testing and debugging algorithms for big data analytics without compromising the performance and efficiency of the underlying DISC framework. To design such techniques, we investigate how we can build interactive and responsive debugging primitives that significantly reduce the debugging time, yet do not pose much performance overhead on big data applications. Furthermore, we investigate how we can leverage data provenance techniques from databases and fault-isolation algorithms from software engineering to pinpoint the minimal subset of failure-inducing inputs efficiently. To improve the reliability of big data analytics, we investigate how we can abstract the semantics of dataflow operators and use them in tandem with the semantics of user-defined functions to generate a minimum set of synthetic test inputs capable of revealing more defects than the entire input dataset.To examine the first hypothesis, we introduce interactive, real-time debugging primitives for big data analytics through innovative and scalable debugging features such as simulated breakpoint, dynamic watchpoint, and crash culprit identification. Second, we design a new automated fault localization approach that combines insights from both the software engineering and database literature to bring delta debugging closer to a reality in the big data applications by leveraging data provenance and by constructing systems optimizations for debugging provenance queries. Lastly, we devise a new symbolic-execution based white-box testing algorithm for big data applications that abstracts the implementation of dataflow operators using logical specifications instead of modeling their implementations and combines them with the semantics of any arbitrary user-defined function. We instantiate the idea of an interactive debugging algorithm as BigDebug, the idea of an automated debugging algorithm as BigSift, and the idea of symbolic execution-based testing as BigTest. Our investigation shows that the interactive debugging primitives can scale to terabytes---our record-level tracing incurs less than 25% overhead on average and provides up to 100% time saving compared to the baseline replay debugger. Second, we observe that by combining data provenance with delta debugging, we can identify the minimum faulty input in just under 30% of the original job execution time. Lastly, we verify that by abstracting dataflow operators using logical specifications, we can efficiently generate the most concise test data suitable for local testing while revealing twice as many faults as prior approaches. Our investigations collectively demonstrate that developer productivity can be significantly improved through effective and scalable testing and debugging techniques for big data analytics, without impacting the DISC framework's performance. This dissertation affirms the feasibility of automated debugging and testing techniques for big data analytics---techniques that were previously considered infeasible for large-scale data processing

Impact of water scarcity on food security at micro level in Pakistan

Pakistan is confronting the problem of water scarcity which is rendering an adverse impact on food security. The study examines the impact of water scarcity on food security in an era of climate change. It further focuses on projecting the future trends of water and food stock. The research effort probes the links among water scarcity, climate change, food security, water security, food inflation, poverty and management of water resources. Data on food security was collected from the FSA (Food Security analysis) of the Sustainable development Policy institute (SDPI) and Food insecurity and Vulnerability Information mapping system (FIVIMS). Logistic equations have been employed to catch the effect of water scarcity on three components of food security separately. In fact, the present study develops a series of models that captures the impact of water scarcity on the components of food security at Micro level. The models have traced an adverse impact of water scarcity water scarcity on food security at Micro level. The findings so obtained may help in proposing the policy guidelines for overcoming water scarcity and handling with food insecurity caused by water scarcity and other factors.Water scarcity, Water supply, Water Demand, Food security, Micro level, Logistic regression

Impact of water scarcity on food security at macro level in Pakistan

Pakistan is confronting the problem of water scarcity which is rendering an adverse impact on food security. The study examines the impact of water scarcity on food security in an era of climate change. It further focuses on projecting the future trends of water and food stock. The research effort probes the links among water scarcity, climate change, food security, water security, food inflation, poverty and management of water resources. Data on food security was collected from the FSA (Food Security analysis) of the Sustainable development Policy institute (SDPI) and Food insecurity and Vulnerability Information mapping system (FIVIMS). Logistic regression equations have been employed to catch the effect of water scarcity on three components of food security separately. In fact, the present study develops a series of models that captures the impact of water scarcity on the components of food security at Macro level.The models has traced an adverse impact of water scarcity water scarcity on food security at macro levels. The findings so obtained may help in proposing the policy guidelines for overcoming water scarcity and handling with food insecurity caused by water scarcity and other factors.Water scarcity, Water supply, Water demand, Food security, Macro level

3D Face Modelling, Analysis and Synthesis

Human faces have always been of a special interest to researchers in the computer vision and graphics areas. There has been an explosion in the number of studies around accurately modelling, analysing and synthesising realistic faces for various applications. The importance of human faces emerges from the fact that they are invaluable means of effective communication, recognition, behaviour analysis, conveying emotions, etc. Therefore, addressing the automatic visual perception of human faces efficiently could open up many influential applications in various domains, e.g. virtual/augmented reality, computer-aided surgeries, security and surveillance, entertainment, and many more. However, the vast variability associated with the geometry and appearance of human faces captured in unconstrained videos and images renders their automatic analysis and understanding very challenging even today. The primary objective of this thesis is to develop novel methodologies of 3D computer vision for human faces that go beyond the state of the art and achieve unprecedented quality and robustness. In more detail, this thesis advances the state of the art in 3D facial shape reconstruction and tracking, fine-grained 3D facial motion estimation, expression recognition and facial synthesis with the aid of 3D face modelling. We give a special attention to the case where the input comes from monocular imagery data captured under uncontrolled settings, a.k.a. \textit{in-the-wild} data. This kind of data are available in abundance nowadays on the internet. Analysing these data pushes the boundaries of currently available computer vision algorithms and opens up many new crucial applications in the industry. We define the four targeted vision problems (3D facial reconstruction $\&$ tracking, fine-grained 3D facial motion estimation, expression recognition, facial synthesis) in this thesis as the four 3D-based essential systems for the automatic facial behaviour understanding and show how they rely on each other. Finally, to aid the research conducted in this thesis, we collect and annotate a large-scale videos dataset of monocular facial performances. All of our proposed methods demonstarte very promising quantitative and qualitative results when compared to the state-of-the-art methods

The Global Threat to Manta and Mobula Rays

Manta and mobula rays span the tropics of the world and are among the most captivating and charismatic of marine species. However, their survival is severely threatened by growing fisheries pressure driven by demand for the gill rakers that the animals use to filter feed. This report is the first global assessment of what is currently known about manta and mobula biology, the threats they face, the fisheries and trade that target them, non-consumptive and sustainable uses for communities to profit from them, current conservation measures and urgent steps recommended to prevent regional extinctions.