The ACM Lifelog Search Challenge (LSC) is an annual comparative benchmarking exercise that brings together researchers in the field of multimedia retrieval to evaluate interactive search systems using a large-scale multimodal lifelog dataset. This paper presents an overview of the ninth edition of the challenge (LSC’26), held as a workshop during the ACM International Conference on Multimedia Retrieval (ICMR ’26) in Amsterdam. To broaden its scope, the workshop now features three submission tracks: the traditional Challenge Track for real-time search performance, a new General Lifelog Research Track for theoretical and architectural advancements, and an additional Open Source Track aimed at enhancing reproducibility and reducing barriers to entry for new participants.

For the sixth time since 2018, the Lifelog Search Challenge (LSC) was organized as a comparative benchmarking exercise for various interactive lifelog search systems. The goal of this international competition is to test system capabilities to access large multimodal lifelogs. LSC’23 attracted twelve participanting teams, each of whom had developed a competitive interactive lifelog retrieval system. The benchmark was organized in front of live audience at the LSC workshop at ACM ICMR’23. As in previous editions, this introductory paper presents the LSC workshop and introduces the participating lifelog search systems.

For the fifth time since 2018, the Lifelog Search Challenge (LSC) facilitated a benchmarking exercise to compare interactive search systems designed for multimodal lifelogs. LSC'22 attracted nine participating research groups who developed interactive lifelog retrieval systems enabling fast and effective access to lifelogs. The systems competed in front of a hybrid audience at the LSC workshop at ACM ICMR'22. This paper presents an introduction to the LSC workshop, the new (larger) dataset used in the competition, and introduces the participating lifelog search systems.

The Lifelog Search Challenge (LSC) is an annual benchmarking challenge for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'21, the fourth challenge, attracted sixteen participants, each of which had developed interactive retrieval systems for large multimodal lifelogs. These interactive retrieval systems participated in a comparative evaluation in front of an online live-audience at the LSC workshop at ACM ICMR'21. This overview presents the motivation for LSC'21, the lifelog dataset used in the competition, and the participating systems.

The Lifelog Search Challenge (LSC) is an annual comparative benchmarking activity for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'20, the third such challenge, attracts fourteen participants with their interactive lifelog retrieval systems. These systems are comparatively evaluated in front of a live-audience at the LSC workshop at ACM ICMR'20 in Dublin, Ireland. This overview motivates the challenge, presents the dataset and system configuration used in the challenge, and briefly presents the participating teams.

Loitering is a suspicious behavior that often leads to criminal actions, such as pickpocketing and illegal entry. Tracking methods can determine suspicious behavior based on trajectory, but require continuous appearance and are difficult to scale up to multi-camera systems. Using the duration of appearance of features works on multiple cameras, but does not consider major aspects of loitering behavior, such as repeated appearance and trajectory of candidates. We introduce an entropy model that maps the location of a person's features on a heatmap. It can be used as an abstraction of trajectory tracking across multiple surveillance cameras. We evaluate our method over several datasets and compare it to other loitering detection methods. The results show that our approach has similar results to state of the art, but can provide additional interesting candidates.

It is very difficult to fully automate the detection of loitering behavior in video surveillance, therefore humans are often required for monitoring. Alternatively, we could provide a list of potential loiterer candidates for a final yes/no judgment of a human operator. Our system, VisLoiter+, realizes this idea with a unique, user-friendly interface and by employing an entropy model for improved loitering analysis. Rather than using only frequency of appearance, we expand the loiter analysis with new methods measuring the amount of person movements across multiple camera views. The interface gives an overview of loiterer candidates to show their behavior at a glance, complemented by a lightweight video playback for further details about why a candidate was selected. We demonstrate that our system outperforms state-of-the-art solutions using real-life data sets.

In this paper we present our findings from an experiment designed to test the effectiveness of complex, thumbnail based layouts and the interaction methods required, in video retrieval scenarios for handheld devices such as smartphones. Our evaluation explores the relationship between the number of thumbnails (Quantity) visible on screen and their discernible detail (Quality) with regards to the related necessary amount of interaction. The results indicate that such layouts achieve a very high rate of accuracy and speed but that the nature of the interaction is of critical importance for the success of the system.

The tremendous amount of data resulting from the regular usage of tools for automatic presentation recording demand for elaborate search functionality. A detailed analysis of the according multimedia documents is required to allow search at a very detailed level. Unfortunately, the produced data differs significantly from traditional documents. In this demo, we discuss the problems appearing in the presentation retrieval scenario and introduce aofSE, a search engine to study and illustrate these problems as well as to develop and present according solutions and new approaches for this task.

In presentation recording special effort is usually put into the automation of the production process, that is in automatically creating high quality data files without much or any need for manual recording and post-editing [5]. With the advent of such systems and their usage in classroom teaching, at conferences, etc., there is an increasing need for techniques and abilities which enable users to search in those documents and to localize some specific information. In this paper we describe how we integrated information retrieval techniques into the Authoring on the Fly (AOF) system, an approach for automatic presentation recording. We have chosen the AOF system for two reasons. On the one hand, it is a well-established way for presentation recording, used by various universities and institutions1. On the other hand it is general enough to illustrate typical problems and challenges a developer is facing when designing a system for information retrieval from multimedia data streams which occur in the presentation recording scenario.