Edge analytics in the internet of things

High-data-rate sensors are becoming ubiquitous in the Internet of Things. GigaSight is an Internet-scale repository of crowd-sourced video content that enforces privacy preferences and access controls. The architecture is a federated system of VM-based cloudlets that perform video analytics at the edge of the Internet

SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos

In this paper, we introduce SoccerNet, a benchmark for action spotting in soccer videos. The dataset is composed of 500 complete soccer games from six main European leagues, covering three seasons from 2014 to 2017 and a total duration of 764 hours. A total of 6,637 temporal annotations are automatically parsed from online match reports at a one minute resolution for three main classes of events (Goal, Yellow/Red Card, and Substitution). As such, the dataset is easily scalable. These annotations are manually refined to a one second resolution by anchoring them at a single timestamp following well-defined soccer rules. With an average of one event every 6.9 minutes, this dataset focuses on the problem of localizing very sparse events within long videos. We define the task of spotting as finding the anchors of soccer events in a video. Making use of recent developments in the realm of generic action recognition and detection in video, we provide strong baselines for detecting soccer events. We show that our best model for classifying temporal segments of length one minute reaches a mean Average Precision (mAP) of 67.8%. For the spotting task, our baseline reaches an Average-mAP of 49.7% for tolerances $\delta$ ranging from 5 to 60 seconds. Our dataset and models are available at https://silviogiancola.github.io/SoccerNet.Comment: CVPR Workshop on Computer Vision in Sports 201

Uncertainty-aware video visual analytics of tracked moving objects

Vast amounts of video data render manual video analysis useless while recent automatic video analytics techniques suffer from insufficient performance. To alleviate these issues we present a scalable and reliable approach exploiting the visual analytics methodology. This involves the user in the iterative process of exploration hypotheses generation and their verification. Scalability is achieved by interactive filter definitions on trajectory features extracted by the automatic computer vision stage. We establish the interface between user and machine adopting the VideoPerpetuoGram (VPG) for visualization and enable users to provide filter-based relevance feedback. Additionally users are supported in deriving hypotheses by context-sensitive statistical graphics. To allow for reliable decision making we gather uncertainties introduced by the computer vision step communicate these information to users through uncertainty visualization and grant fuzzy hypothesis formulation to interact with the machine. Finally we demonstrate the effectiveness of our approach by the video analysis mini challenge which was part of the IEEE Symposium on Visual Analytics Science and Technology 2009

Detect the unexpected: a science for surveillance

Purpose – The purpose of this paper is to outline a strategy for research development focused on addressing the neglected role of visual perception in real life tasks such as policing surveillance and command and control settings. Approach – The scale of surveillance task in modern control room is expanding as technology increases input capacity at an accelerating rate. The authors review recent literature highlighting the difficulties that apply to modern surveillance and give examples of how poor detection of the unexpected can be, and how surprising this deficit can be. Perceptual phenomena such as change blindness are linked to the perceptual processes undertaken by law-enforcement personnel. Findings – A scientific programme is outlined for how detection deficits can best be addressed in the context of a multidisciplinary collaborative agenda between researchers and practitioners. The development of a cognitive research field specifically examining the occurrence of perceptual “failures” provides an opportunity for policing agencies to relate laboratory findings in psychology to their own fields of day-to-day enquiry. Originality/value – The paper shows, with examples, where interdisciplinary research may best be focussed on evaluating practical solutions and on generating useable guidelines on procedure and practice. It also argues that these processes should be investigated in real and simulated context-specific studies to confirm the validity of the findings in these new applied scenarios

A Large-scale Distributed Video Parsing and Evaluation Platform

Visual surveillance systems have become one of the largest data sources of Big Visual Data in real world. However, existing systems for video analysis still lack the ability to handle the problems of scalability, expansibility and error-prone, though great advances have been achieved in a number of visual recognition tasks and surveillance applications, e.g., pedestrian/vehicle detection, people/vehicle counting. Moreover, few algorithms explore the specific values/characteristics in large-scale surveillance videos. To address these problems in large-scale video analysis, we develop a scalable video parsing and evaluation platform through combining some advanced techniques for Big Data processing, including Spark Streaming, Kafka and Hadoop Distributed Filesystem (HDFS). Also, a Web User Interface is designed in the system, to collect users' degrees of satisfaction on the recognition tasks so as to evaluate the performance of the whole system. Furthermore, the highly extensible platform running on the long-term surveillance videos makes it possible to develop more intelligent incremental algorithms to enhance the performance of various visual recognition tasks.Comment: Accepted by Chinese Conference on Intelligent Visual Surveillance 201