Video Search Research Articles

Navigating Alma’s Gang Culture: Exploring Testimonio, Identity, and Violence through an Interactive Documentary

Research Article| October 01 2020 Navigating Alma’s Gang Culture: Exploring Testimonio, Identity, and Violence through an Interactive Documentary Heather McIntosh; Heather McIntosh Heather McIntosh is an associate professor in the mass communication department at Minnesota State University, Mankato. Her research and teaching focus on interactive documentary production, game design and development, and social media. Search for other works by this author on: This Site Google Kalen Churcher Kalen Churcher Kalen Churcher is an associate professor in the communication studies department at Wilkes University, Wilkes-Barre, Pennsylvania. Her research lies at the intersection of journalism and cultural studies. In particular, she explores the use of media by marginalized groups. Search for other works by this author on: This Site Google Journal of Film and Video (2020) 72 (3-4): 16–31. https://doi.org/10.5406/jfilmvideo.72.3-4.0016 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Heather McIntosh, Kalen Churcher; Navigating Alma’s Gang Culture: Exploring Testimonio, Identity, and Violence through an Interactive Documentary. Journal of Film and Video 1 January 2020; 72 (3-4): 16–31. doi: https://doi.org/10.5406/jfilmvideo.72.3-4.0016 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressJournal of Film and Video Search Advanced Search The text of this article is only available as a PDF. Copyright 2020 by the Board of Trustees of the University of Illinois2020 Article PDF first page preview Close Modal You do not currently have access to this content.

The Fix Is In: Dubbing as Transcultural and Transmedia Adaptation

Research Article| October 01 2020 The Fix Is In: Dubbing as Transcultural and Transmedia Adaptation Daniel Johnson Daniel Johnson Daniel Johnson received his PhD from the joint program in East Asian Cinema from the University of Chicago. He teaches in the Department of East Asian Studies at New York University, and his work has appeared in journals such as Games and Culture, Japanese Studies, and Critical Studies in Television. Search for other works by this author on: This Site Google Journal of Film and Video (2020) 72 (3-4): 3–15. https://doi.org/10.5406/jfilmvideo.72.3-4.0003 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Daniel Johnson; The Fix Is In: Dubbing as Transcultural and Transmedia Adaptation. Journal of Film and Video 1 January 2020; 72 (3-4): 3–15. doi: https://doi.org/10.5406/jfilmvideo.72.3-4.0003 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressJournal of Film and Video Search Advanced Search The text of this article is only available as a PDF. Copyright 2020 by the Board of Trustees of the University of Illinois2020 Article PDF first page preview Close Modal You do not currently have access to this content.

Challenging Ontological Order: Walls and Historical Space in Narrative Films

Research Article| October 01 2020 Challenging Ontological Order: Walls and Historical Space in Narrative Films Odeya Kohen Raz Odeya Kohen Raz Odeya Kohen Raz is a senior lecturer in the School of Audio and Visual Arts, Sapir Academic College. She also teaches in the Steve Tisch School for Film and Television at Tel Aviv University and is a teaching coordinator in the Department of Literature, Language and the Arts at the Open University of Israel. Search for other works by this author on: This Site Google Journal of Film and Video (2020) 72 (3-4): 32–47. https://doi.org/10.5406/jfilmvideo.72.3-4.0032 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Odeya Kohen Raz; Challenging Ontological Order: Walls and Historical Space in Narrative Films. Journal of Film and Video 1 January 2020; 72 (3-4): 32–47. doi: https://doi.org/10.5406/jfilmvideo.72.3-4.0032 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressJournal of Film and Video Search Advanced Search The text of this article is only available as a PDF. Copyright 2020 by the Board of Trustees of the University of Illinois2020 Article PDF first page preview Close Modal You do not currently have access to this content.

Xigua Video as a Source of Information on Breast Cancer: Content Analysis.

BackgroundSeeking health information on the internet is a popular trend. Xigua Video, a short video platform in China, ranks among the most accessed websites in the country and hosts an increasing number of videos with medical information. However, the nature of these videos is frequently unscientific, misleading, or even harmful.ObjectiveLittle is known about Xigua Video as a source of information on breast cancer. Thus, the study aimed to investigate the contents, quality, and reliability of breast cancer–related content on Xigua Video.MethodsOn February 4, 2020, a Xigua Video search was performed using the keyword “breast cancer.” Videos were categorized by 2 doctors based on whether the video content provided useful or misleading information. Furthermore, the reliability and quality of the videos were assessed using the 5-point DISCERN tool and 5-point global quality score criteria.ResultsOut of the 170 videos selected for the study, 64 (37.6%) were classified as useful, whereas 106 (62.4%) provided misleading information. A total of 41.8% videos (71/170) were generated by individuals compared to 19.4% videos (33/170) contributed by health care professionals. The topics mainly covered etiology, anatomy, symptoms, preventions, treatments, and prognosis. The top topic was “treatments” (119/170, 70%). The reliability scores and global quality scores of the videos in the useful information group were high (P<.001). No differences were observed between the 2 groups in terms of video length, duration in months, and comments. The number of total views was higher for the misleading information group (819,478.5 vs 647,940) but did not reach a level of statistical significance (P=.112). The uploading sources of the videos were mainly health care professionals, health information websites, medical advertisements, and individuals. Statistical differences were found between the uploading source groups in terms of reliability scores and global quality scores (P<.001). In terms of total views, video length, duration, and comments, no statistical differences were indicated among the said groups. However, a statistical difference was noted between the useful and misleading information video groups with respect to the uploading sources (P<.001).ConclusionsA large number of Xigua videos pertaining to breast cancer contain misleading information. There is a need for accurate health information to be provided on Xigua Video and other social media; health care professionals should address this challenge.

Multi-Proxy Constraint Loss for Vehicle Re-Identification.

Vehicle re-identification plays an important role in cross-camera tracking and vehicle search in surveillance videos. Large variance in the appearance of the same vehicle captured by different cameras and high similarity of different vehicles with the same model poses challenges for vehicle re-identification. Most existing methods use a center proxy to represent a vehicle identity; however, the intra-class variance leads to great difficulty in fitting images of the same identity to one center feature and the images with high similarity belonging to different identities cannot be separated effectively. In this paper, we propose a sampling strategy considering different viewpoints and a multi-proxy constraint loss function which represents a class with multiple proxies to perform different constraints on images of the same vehicle from different viewpoints. Our proposed sampling strategy contributes to better mine samples corresponding to different proxies in a mini-batch using the camera information. The multi-proxy constraint loss function pulls the image towards the furthest proxy of the same class and pushes the image from the nearest proxy of different class further away, resulting in a larger margin between decision boundaries. Extensive experiments on two large-scale vehicle datasets (VeRi and VehicleID) demonstrate that our learned global features using a single-branch network outperforms previous works with more complicated network and those that further re-rank with spatio-temporal information. In addition, our method is easy to plug into other classification methods to improve the performance.

A Cross-Sectional Study of the Portrayal of Vocal Health in YouTube Videos

PurposeThe current study examines metadata, upload source, type of informational content, understandability, and actionability of YouTube videos related to vocal health.MethodA search for videos related to vocal health in YouTube was completed. Metadata (i.e., number of views, video length, thumbs-up, thumbs-down), upload source (i.e., consumer, professional, media), and type of informational content were identified. The Patient Education Material Assessment Tool for Audiovisual Materials was used to assess understandability and actionability of the 100 most frequently viewed videos.ResultsA total of 166 videos were obtained. The collective number of views was 19,799,299. A significant difference between video source groups was found for number of views and thumbs-down, but not for video length or thumbs-up. The YouTube videos were primarily educational, and a majority of the content focused on tips and techniques for professional voice users. Videos had adequate understandability (i.e., 71.5%) and actionability scores (i.e., 74%). Videos uploaded by consumers were superior to professional sources in actionability, but no difference was noted between video source for understandability.ConclusionsStudy insights about metadata, source, type of informational content, understandability, and actionability of YouTube videos may help professionals understand the nature of online content related to vocal health. Study implications and recommendations for further research are discussed.

Proning Patients With COVID-19: A Review of Equipment and Methods.

ObjectiveTo identify and critically evaluate methods for proning patients with COVID-19 in the intensive care unit (ICU).BackgroundAcute respiratory distress syndrome (ARDS) is common in hospitalized patients with COVID-19. Proning improves blood oxygenation and survival rates in these patients but is not commonly performed due to the difficulty of the procedure.MethodsAn academic literature review, internet video search, and consultation with five subject-matter experts was performed to identify known methods for proning. Evaluation of each method considered the number of healthcare workers required, physical stresses on staff, risk of adverse events to patients, and equipment cost and availability.ResultsSeveral variations of manual techniques and-lift assisted techniques were identified in addition to a specialized proning bed. Manual methods require more healthcare workers, higher physical stresses, and greater risk of adverse events than lift-assisted methods or the proning bed.ConclusionBoth the specialized proning bed and a lift-assisted method using straps largely eliminated manual forces required for proning while allowing for a controlled lowering and positioning of the patient.ApplicationThis review will guide practitioners to the most suitable methods for proning patients in the ICU.

Young People Leisure Activities Transformation during Quarantine Self-Isolation: Characteristics and Regulation Problem

The objective of the research is to study the characteristics of leisure activities of young people in self-isolated conditions during the coronavirus pandemic. As a research method, the authors use an online survey that allowed them to more effectively visualize the features of leisure activities and the peculiarities of their regulation among self-isolated students. The article also considers the leisure strategies of young people in conditions of self-isolation and social fortune. The novelty of the research lies in the fact that the hierarchy of leisure preferences of young people was studied for the first time during the period of self-isolation. It is concluded that the leisure practices of young people are identified by the regular use of the Internet, instead of the use of sports or any other practice. The priority areas of the Internet application are: study and communication, reading books and consulting news. In addition, there is the ability to communicate on social networks, search for video and audio recordings and games on the network. Preferred game genres are shown to be multiplatform simulators and strategies.

Retracted: Design of Target Detection and Tracking System for Sports Video

Video motion target tracking has been widely used in the field of sports. Content-based video search has a broad application prospect and attracts more and more researchers’ attention. In view of the shortcomings of moving object detection in motion video at present, a human motion recognition method based on correlation vector machine is proposed. Combined with the improved Gaussian mixture model of moving object detection and tracking method in motion video, moving object detection and tracking are carried out. The design of the system is realized. Firstly, it collects sports videos, considers all kinds of sports objects in sports videos, uses improved background update difference method to detect sports objects, then uses correlation vector machine to recognize human motion, and finally uses AdaBoost classifier to use it in multiple sports videos and sports objects. Carry out follow-up experiments. Results in this way, we can track the sports target in sports video accurately and quickly, and the real-time performance of sports target tracking is better than other tracking methods, which provides a new research tool for tracking sports target in sports video.

Assessing the Educational Quality of Facebook Videos as an Informative Resource on Otitis Media.

This study analyzes the quality and reliability of otitis media (OM) videos on Facebook and investigates whether the videos shared within the Facebook community are considered to be a valuable educational tool. The results of this study are important for providing clinicians with the necessary understanding about the video content that their patients may be exposed to. Cross-sectional analysis of video content. A new Facebook account was created to carry out a search for videos on OM. Inclusion criteria were as follows: videos intended for educating patients or guardians on OM, videos in the English language, and videos with at least 1 share. A total of 364 videos were screened, and 62 fit our inclusion criteria for analysis. The majority (56%) of OM videos on Facebook focused on complementary and alternative medication without mentioning any current guidelines. A limited amount of videos (29%) made any mention to surgical treatment options for OM. There was a strong positive correlation (rho = 0.8419, P < .001) between a video's content and its reliability. There was no correlation seen between a video's content and its shares (rho = -0.142, P = .1359). The majority of OM videos on Facebook are inadequate for educational value. Clinicians should know about the existence of videos on OM and the quality of information that parents are exposed to.

Navigating four billion videos: teacher search strategies and the YouTube algorithm

ABSTRACT YouTube hosts a vast catalogue of instructional videos that are increasingly used in formal education contexts. Teachers regularly use YouTube to select videos for students, but the processes they use to select these resources have been understudied. This study explores how teachers search for videos, and the role of YouTube’s complex algorithm in that process. It reports on a case study of nine Australian teachers working across two schools, at various stages of their careers, from a variety of subject domains. When searching for content on YouTube, most teachers use what can be described as a search and scroll method, meaning they enter a relatively simple search term then scroll through the resulting list. This strategy relies on the teacher to select appropriate videos displayed by YouTube’s algorithm, representing an entanglement of human and software labour. The considerations of teachers around specificity to learning goals, length, engagement, and affect when choosing videos are discussed as important factors in final selection. While some teachers navigated through the search returns using well-developed content knowledge, others used rushed, or uncritical methods, increasing the influence of the algorithm in their practice.

Systematic assessment of information about surgical urinary stone treatment on YouTube.

To systematically assess the quality of videos on the surgical treatment of urinary stones available on YouTube using validated instruments. A systematic search for videos on YouTube addressing treatment options of urinary stones was performed in October 2019. Assessed parameters included basic data (e.g. number of views), the grade of misinformation reporting of conflicts of interest. Quality of content was analyzed using the validated DISCERN questionnaire. Data were analyzed using descriptive statistics. A total of 100 videos with a median of 26,234 views (1020-1,720,521) were included in the analysis. Of these, only 26 videos were rated to contain no misinformation and only nine disclosed potential conflicts of interest. Overall, the median quality of the videos was low (2 out of 5 points for DISCERN question 16). Videos uploaded by healthcare professionals and medical societies/organizations offered significantly higher levels of quality. In particular, the videos provided by the EAU achieved the highest rating with a median score of 3.0. The majority of videos concerning the surgical treatment of urinary stones have a low quality of content, are potentially subject to commercial bias and do not report on conflicts of interest. Videos provided by medical societies, such as the EAU, provide a higher level of quality. This highlights the importance of active recommendation of evidence-based patient education materials.

Shuffled ImageNet Banks for Video Event Detection and Search

This article aims for the detection and search of events in videos, where video examples are either scarce or even absent during training. To enable such event detection and search, ImageNet concept banks have shown to be effective. Rather than employing the standard concept bank of 1,000 ImageNet classes, we leverage the full 21,841-class dataset. We identify two problems with using the full dataset: (i) there is an imbalance between the number of examples per concept, and (ii) not all concepts are equally relevant for events. In this article, we propose to balance large-scale image hierarchies for pre-training. We shuffle concepts based on bottom-up and top-down operations to overcome the problems of example imbalance and concept relevance. Using this strategy, we arrive at the shuffled ImageNet bank, a concept bank with an order of magnitude more concepts compared to standard ImageNet banks. Compared to standard ImageNet pre-training, our shuffles result in more discriminative representations to train event models from the limited video event examples. For event search, the broad range of concepts enable a closer match between textual queries of events and concept detections in videos. Experimentally, we show the benefit of the proposed bank for event detection and event search, with state-of-the-art performance for both tasks on the challenging TRECVID Multimedia Event Detection and Ad-Hoc Video Search benchmarks.

Forming a TED talks sphere for convenient search

Purpose TED (Technology, Entertainment, Design, www.ted.com/) Talks has been one of the most popular video systems. However, the current TED Talks system expressed its inquired videos as in a two-dimensional (2D) table, which is inconvenient for searching the relationships among videos and tags. This study converts the TED Talks table into a sphere by using optimization techniques to help users search for preferred videos. Design/methodology/approach There are five phases in this study as follows. Phase 1: Reorganize data of 36 tags and 108 videos; Phase 2: Allocate tags on the TED sphere; Phase 3: Allocate videos on the TED sphere; Phase 4: Develop an online interactive TED retrieval system; and Phase 5: Perform survey and evaluation. Findings One survey demonstrated that the TED Talks sphere is more convenient for searching videos, as it is more user-friendly because of its graphical user interface, more convenient to use, more useful for retrieving information and can facilitate a more responsive search for users’ preferred videos. Research limitations/implications The numbers of tags and videos able to be displayed on a sphere is limited by the capacity of an optimization software and hardware. Practical implications The proposed sphere system can be used by a large number of users of TED Talks groups. Social implications This sphere systems can also be applied to other fields which use 2D forms to display the relationships among objects. Originality/value This study uses an optimization method to convert a 2D form into a 3D sphere to highlight the relationships among numerous objects.

A General Framework for Deep Supervised Discrete Hashing

With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefiting from recent advances in deep learning, deep hashing methods have shown superior performance over the traditional hashing methods. However, there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited). In this paper, we develop a general deep supervised discrete hashing framework based on the assumption that the learned binary codes should be ideal for classification. Both the similarity information and the classification information are used to learn the hash codes within one stream framework. We constrain the outputs of the last layer to be binary codes directly, which is rarely investigated in deep hashing algorithms. Besides, both the pairwise similarity information and the triplet ranking information are exploited in this paper. In addition, two different loss functions are presented: $${l_2}$$ loss and hinge loss, which are carefully designed for the classification term under the one stream framework. Because of the discrete nature of hash codes, an alternating minimization method is used to optimize the objective function. Experimental results have shown that our approach outperforms current state-of-the-art methods on benchmark datasets.

Information on surgical treatment of benign prostatic hyperplasia on YouTube is highly biased and misleading.

To assess the quality of videos on the surgical treatment of lower urinary tract symptoms associated with benign prostatic hyperplasia (LUTS/BPH) available on YouTube, given that such video-sharing platforms are frequently used as sources of patient information and the therapeutic landscape of LUTS/BPH has evolved substantially during recent years. A systematic search for videos on YouTube addressing treatment options for LUTS/BPH was performed in May 2019. Measures assessed included basic data (e.g. number of views), grade of misinformation and reporting of conflicts of interest. The quality of content was analysed using the validated DISCERN questionnaire. Data were analysed using descriptive statistics. A total of 159 videos with a median (range) of 8570 (648-2384391) views were included in the analysis. Only 21 videos (13.2%) were rated as containing no misinformation, 26 (16.4%) were free of commercial bias, and two (1.3%) disclosed potential conflicts of interest. According to DISCERN, the median overall quality of the videos was low (2 out of 5 points for question 16). Only four of the 15 assessed categories (bipolar and holmium laser enucleation of the prostate, transurethral resection of the prostate and patient-based search terms) were scored as having moderate median overall quality (3 points). Most videos on the surgical treatment of LUTS/BPH on YouTube had a low quality of content, provided misinformation, were subject to commercial bias and did not report on conflicts of interest. These findings emphasize the importance of thorough doctor-patient communication and active recommendation of unbiased patient education materials.

Activist Praxis Meets Project-Based Learning: A Case Study of Student Involvement at the Chicago Feminist Film Festival

Research Article| April 01 2020 Activist Praxis Meets Project-Based Learning: A Case Study of Student Involvement at the Chicago Feminist Film Festival Susan Kerns; Susan Kerns Susan Santha Kerns, PhD, is a filmmaker and an associate professor in the Cinema and Television Arts Department of Columbia College Chicago. She is cofounder and codirector of the Chicago Feminist Film Festival and works in acquisitions for Cow Lamp Films. Her academic writing has appeared in Journal of Graphic Novels and Comics, Comunicazioni Sociali, and The Routledge Handbook of Contemporary Feminism. Search for other works by this author on: This Site Google Michelle Yates Michelle Yates Michelle Yates, PhD, is an associate professor of cultural studies and humanities at Columbia College Chicago. She is the cofounder and codirector of the Chicago Feminist Film Festival and has published work in Antipode: A Radical Journal of Geography, ISLE: Interdisciplinary Studies in Literature and Environment, Science Fiction Film and Television, and edited collections such as The SAGE Handbook for Frankfurt School Critical Theory. Search for other works by this author on: This Site Google Journal of Film and Video (2020) 72 (1-2): 67–72. https://doi.org/10.5406/jfilmvideo.72.1-2.0067 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Susan Kerns, Michelle Yates; Activist Praxis Meets Project-Based Learning: A Case Study of Student Involvement at the Chicago Feminist Film Festival. Journal of Film and Video 1 January 2020; 72 (1-2): 67–72. doi: https://doi.org/10.5406/jfilmvideo.72.1-2.0067 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressJournal of Film and Video Search Advanced Search The text of this article is only available as a PDF. Copyright 2020 by the Board of Trustees of the University of Illinois2020 Article PDF first page preview Close Modal You do not currently have access to this content.

The World Screen: Japan’s Cinematic Reinvention and International Film Festivals

Research Article| April 01 2020 The World Screen: Japan’s Cinematic Reinvention and International Film Festivals Lance Lomax Lance Lomax Lance Lomax is a doctoral student in the English Department at Texas Tech University. His research interests include transpacific media, adaptation, and the industrial and generic aspects of Japanese cinema. Search for other works by this author on: This Site Google Journal of Film and Video (2020) 72 (1-2): 46–57. https://doi.org/10.5406/jfilmvideo.72.1-2.0046 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Lance Lomax; The World Screen: Japan’s Cinematic Reinvention and International Film Festivals. Journal of Film and Video 1 January 2020; 72 (1-2): 46–57. doi: https://doi.org/10.5406/jfilmvideo.72.1-2.0046 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressJournal of Film and Video Search Advanced Search The text of this article is only available as a PDF. Copyright 2020 by the Board of Trustees of the University of Illinois2020 Article PDF first page preview Close Modal You do not currently have access to this content.

A supervised deep convolutional based bidirectional long short term memory video hashing for large scale video retrieval applications

Recently, large scale video content retrieval has gained more attention due to a large amount of user-generated images and video content available over the internet. Hashing is one of the effective techniques that encode the high dimensional features vectors into compact binary codes. The aim of hashing is to generate the short binary codes and map the similar hash code values to retrieve similar video from the database with minimum distance measure. Deep learning-based hashing networks are employed to learn the representative video feature to estimate the hash functions. However, the existing hashing approaches fail due to the frame-level feature representation and does not well exploits the effective temporal features in visual search. Furthermore, significant loss of features during the dimensionality reduction step causes low accuracy. Hence, it is essential to develop a deep learning-based hashing framework that should exploits both the strong spatial and temporal features for the scalable video search. The main objective is to learn the high dimensional features from the entire video and derive compact binary codes to retrieve the similar videos for the input query sequence. In this paper, we propose a joint network model of supervised stacked heterogeneous convolutional multi-kernel (Stacked HetConv-MK)-bidirectional Long Short Term Memory (BiDLSTM) network model that effectively encodes the rich structural as well as the discriminative features from the video sequence to estimate the compact binary codes. Initially, the video frames are passed to the stacked convolution networks with heterogeneous convolutional kernel size and residual learning to extract the spatial features at different views from the video sequences and to improve the learning efficiency. Then, the bidirectional network computes the sequence in both forward and backward directions and obtains the series of hidden state output. Finally, the fully connected structure with an activation unit performs hashing to learn the multiple codes for each video. Experimental analysis is performed on three datasets and the result shows a better accuracy measure than the other state-of-art approaches.

Interactive Video Retrieval in the Age of Deep Learning – Detailed Evaluation of VBS 2019

Despite the fact that automatic content analysis has made remarkable progress over the last decade – mainly due to significant advances in machine learning – interactive video retrieval is still a very challenging problem, with an increasing relevance in practical applications. The Video Browser Showdown (VBS) is an annual evaluation competition that pushes the limits of interactive video retrieval with state-of-the-art tools, tasks, data, and evaluation metrics. In this paper, we analyse the results and outcome of the 8th iteration of the VBS in detail. We first give an overview of the novel and considerably larger V3C1 dataset and the tasks that were performed during VBS 2019. We then go on to describe the search systems of the six international teams in terms of features and performance. And finally, we perform an in-depth analysis of the per-team success ratio and relate this to the search strategies that were applied, the most popular features, and problems that were experienced. A large part of this analysis was conducted based on logs that were collected during the competition itself. This analysis gives further insights into the typical search behavior and differences between expert and novice users. Our evaluation shows that textual search and content browsing are the most important aspects in terms of logged user interactions. Furthermore, we observe a trend towards deep learning based features, especially in the form of labels generated by artificial neural networks. But nevertheless, for some tasks, very specific content-based search features are still being used. We expect these findings to contribute to future improvements of interactive video search systems.