scholarly journals On the impurity of street-scene video footage

2015 ◽  
Author(s):  
C. Henderson ◽  
S.G. Blasi ◽  
F. Sobhani ◽  
E. Izquierdo
Keyword(s):  
Video Footage ◽  
Street Scene

Heritage

2018 ◽  
Author(s):  
Omar Shaikh ◽  
Stefano Bonino
Keyword(s):  
Facial Expressions ◽  
South Asian ◽  
Online Video ◽  
The South ◽  
Men And Women ◽  
The Public ◽  
Video Footage ◽  
Community Heritage ◽  
Personal Accounts ◽  
The Way

The Colourful Heritage Project (CHP) is the first community heritage focused charitable initiative in Scotland aiming to preserve and to celebrate the contributions of early South Asian and Muslim migrants to Scotland. It has successfully collated a considerable number of oral stories to create an online video archive, providing first-hand accounts of the personal journeys and emotions of the arrival of the earliest generation of these migrants in Scotland and highlighting the inspiring lessons that can be learnt from them. The CHP’s aims are first to capture these stories, second to celebrate the community’s achievements, and third to inspire present and future South Asian, Muslim and Scottish generations. It is a community-led charitable project that has been actively documenting a collection of inspirational stories and personal accounts, uniquely told by the protagonists themselves, describing at first hand their stories and adventures. These range all the way from the time of partition itself to resettling in Pakistan, and then to their final accounts of arriving in Scotland. The video footage enables the public to see their facial expressions, feel their emotions and hear their voices, creating poignant memories of these great men and women, and helping to gain a better understanding of the South Asian and Muslim community’s earliest days in Scotland.

Understanding of social behaviour in human collective motions with non-trivial rule of control

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 84-86
Author(s):  
Keisuke Fujii
Keyword(s):  
Machine Learning ◽  
Team Sports ◽  
Machine Learning Techniques ◽  
Theoretical Modelling ◽  
Video Footage ◽  
Learning Techniques ◽  
Collective Motions ◽  
Tracking Movements ◽  
Or Team ◽  
Computational Ability

The coordination and movement of people in large crowds, during sports games or when socialising, seems readily explicable. Sometimes this occurs according to specific rules or instructions such as in a sport or game, at other times the motivations for movement may be more focused around an individual's needs or fears. Over the last decade, the computational ability to identify and track a given individual in video footage has increased. The conventional methods of how data is gathered and interpreted in biology rely on fitting statistical results to particular models or hypotheses. However, data from tracking movements in social groups or team sports are so complex as they cannot easily analyse the vast amounts of information and highly varied patterns. The author is an expert in human behaviour and machine learning who is based at the Graduate School of Informatics at Nagoya University. His challenge is to bridge the gap between rule-based theoretical modelling and data-driven modelling. He is employing machine learning techniques to attempt to solve this problem, as a visiting scientist in RIKEN Center for Advanced Intelligence Project.

scholarly journals Padded Headgear does not Reduce the Incidence of Match Concussions in Professional Men’s Rugby Union: A Case-control Study of 417 Cases

2021 ◽  
Author(s):  
Keith A. Stokes ◽  
Matthew Cross ◽  
Sean Williams ◽  
Carly McKay ◽  
Brent E. Hagel ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Head Injuries ◽  
Median Number ◽  
Case Control ◽  
Rugby Union ◽  
Return To Play ◽  
Injury Incidence ◽  
Video Footage ◽  
Four Seasons ◽  
Nested Case Control

AbstractConcussion is the most common match injury in rugby union. Some players wear padded headgear, but whether this protects against concussion is unclear. In professional male rugby union players, we examined: (i) the association between the use of headgear and match concussion injury incidence, and (ii) whether wearing headgear influenced time to return to play following concussion. Using a nested case-control within a cohort study, four seasons (2013–2017) of injury data from 1117 players at the highest level of rugby union in England were included. Cases were physician-diagnosed concussion injuries. Controls were other contact injuries (excluding all head injuries). We determined headgear use by viewing video footage. Sixteen percent of cases and controls wore headgear. Headgear use had no significant effect on concussion injury incidence (adjusted odds ratio=1.05, 95% CI: 0.71–1.56). Median number of days absent for concussion whilst wearing headgear was 8 days, compared with 7 days without headgear. Having sustained a concussion in the current or previous season increased the odds of concussion more than four-fold (odds ratio=4.55, 95% CI: 3.77–5.49). Wearing headgear was not associated with lower odds of concussions or a reduced number of days' absence following a concussion.

On The Field: Examining Differences in Video Format in Sports Media Viewing

2020 ◽  
Vol 64 (1) ◽  
pp. 781-785
Author(s):  
Ragan Wilson ◽  
Christopher B. Mayhorn
Keyword(s):  
Factor Analysis ◽  
Confirmatory Factor Analysis ◽  
Spatial Presence ◽  
Sports Media ◽  
User Perceptions ◽  
Video Footage ◽  
Confirmatory Factor ◽  
Need To Evaluate ◽  
The Relationship ◽  
Video Format

With virtual reality’s emerging popularity and the subsequent push for more sports media experiences, there is a need to evaluate virtual reality’s use into more video watching experiences. This research explores differences in experiences between Monitor (2D) video and HMD (360-Degree) video footage by measuring user perceptions of presence, suspense, and enjoyment. Furthermore, this study examines the relationship between presence, game attractiveness, suspense, and enjoyment as explored by Kim, Cheong, and Kim (2016). Differences were assessed via a MANOVA examining specifically presence, suspense, and enjoyment while the relationships were explored via a confirmatory factor analysis. Results suggest that there was a difference between Monitor (2D) video and HMD (360-Degree) in regard to spatial presence, engagement, suspense, and enjoyment, but the previous model from Kim et al. (2016) was not a good fit to this study’s data.

scholarly journals Natural Language Description of Videos for Smart Surveillance

2021 ◽  
Vol 11 (9) ◽  
pp. 3730
Author(s):  
Aniqa Dilawari ◽  
Muhammad Usman Ghani Khan ◽  
Yasser D. Al-Otaibi ◽  
Zahoor-ur Rehman ◽  
Atta-ur Rahman ◽  
...  
Keyword(s):  
Natural Language ◽  
Feature Recognition ◽  
Scene Recognition ◽  
Video Data ◽  
Surveillance Video ◽  
Video Footage ◽  
Parallel Pipeline ◽  
September 11 Attacks ◽  
Description Framework ◽  
High Level

After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECViD, UET Video Surveillance (UETVS) and AGRIINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.

Analysis of Vehicle–Pedestrian Interactive Behaviors near Unsignalized Crosswalk

2021 ◽  
pp. 036119812199906
Author(s):  
Byeongjoon Noh ◽  
Dongho Ka ◽  
David Lee ◽  
Hwasoo Yeo
Keyword(s):  
South Korea ◽  
Traffic Accidents ◽  
Road Traffic ◽  
The Road ◽  
Video Footage ◽  
Premature Deaths ◽  
Vehicle Velocity ◽  
Behavioral Features ◽  
Velocity Changes ◽  
On The Road

Road traffic accidents are a leading cause of premature deaths and globally pose a severe threat to human lives. In particular, pedestrians crossing the road present a major cause of vehicle–pedestrian accidents in South Korea, but we lack dense behavioral data to understand the risk they face. This paper proposes a new analytical system for potential pedestrian risk scenes based on video footage obtained by road security cameras already deployed at unsignalized crosswalks. The system can automatically extract the behavioral features of vehicles and pedestrians, affecting the likelihood of potentially dangerous situations after detecting them in individual objects. With these features, we can analyze the movement patterns of vehicles and pedestrians at individual sites, and understand where potential traffic risk scenes occur frequently. Experiments were conducted on four selected behavioral features: vehicle velocity, pedestrian position, vehicle–pedestrian distance, and vehicle–crosswalk distance. Then, to show how they can be useful for monitoring the traffic behaviors on the road, the features are visualized and interpreted to show how they may or may not contribute to potential pedestrian risks at these crosswalks: (i) by analyzing vehicle velocity changes near the crosswalk when there are no pedestrians present; and (ii) analyzing vehicle velocities by vehicle–pedestrian distances when pedestrians are on the crosswalk. The feasibility of the proposed system is validated by applying the system to multiple unsignalized crosswalks in Osan city, South Korea.

scholarly journals Semantic Segmentation of Urban Street Scene Based on Convolutional Neural Network

2020 ◽  
Vol 1682 ◽  
pp. 012077
Author(s):  
Tingting Li ◽  
Chunshan Jiang ◽  
Zhenqi Bian ◽  
Mingchang Wang ◽  
Xuefeng Niu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Semantic Segmentation ◽  
Urban Street ◽  
Street Scene
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