arXiv:2404.08264v1 Announce Type: new
Abstract: Observations with distributed sensors are essential in analyzing a series of human and machine activities (referred to as ‘events’ in this paper) in complex and extensive real-world environments. This is because the information obtained from a single sensor is often missing or fragmented in such an environment; observations from multiple locations and modalities should be integrated to analyze events comprehensively. However, a learning method has yet to be established to extract joint representations that effectively combine such distributed observations. Therefore, we propose Guided Masked sELf-Distillation modeling (Guided-MELD) for inter-sensor relationship modeling. The basic idea of Guided-MELD is to learn to supplement the information from the masked sensor with information from other sensors needed to detect the event. Guided-MELD is expected to enable the system to effectively distill the fragmented or redundant target event information obtained by the sensors without being overly dependent on any specific sensors. To validate the effectiveness of the proposed method in novel tasks of distributed multimedia sensor event analysis, we recorded two new datasets that fit the problem setting: MM-Store and MM-Office. These datasets consist of human activities in a convenience store and an office, recorded using distributed cameras and microphones. Experimental results on these datasets show that the proposed Guided-MELD improves event tagging and detection performance and outperforms conventional inter-sensor relationship modeling methods. Furthermore, the proposed method performed robustly even when sensors were reduced.
The content of this article discusses the importance of distributed sensors in analyzing events in complex real-world environments. It points out that relying on information from a single sensor is often insufficient, and suggests that observations from multiple sensors should be integrated to comprehensively analyze events. This is where the concept of Guided-MELD comes in as a learning method to effectively combine distributed observations.
Guided-MELD stands for Guided Masked sELf-Distillation modeling, a technique that aims to supplement the information from a masked sensor with information from other sensors in order to detect events. By distilling the fragmented or redundant target event information obtained by the sensors, Guided-MELD enables the system to effectively analyze events without being overly dependent on any specific sensor.
In terms of the wider field of multimedia information systems, the concept of distributed sensors and integrating data from multiple locations and modalities is crucial. This approach allows for a more comprehensive analysis of events, especially in complex and extensive real-world environments. The use of Guided-MELD adds another layer to this analysis by providing a method to effectively distill and combine information from different sensors.
Moreover, the article highlights the importance of multi-disciplinary concepts such as Animations, Artificial Reality, Augmented Reality, and Virtual Realities in the context of event analysis. These technologies and techniques can contribute to enhancing the capabilities of distributed sensors and improving the accuracy of event tagging and detection.
To validate the effectiveness of the proposed method, the authors recorded two new datasets: MM-Store and MM-Office. These datasets consist of human activities in a convenience store and an office, recorded using distributed cameras and microphones. The experimental results on these datasets demonstrate that Guided-MELD improves event tagging and detection performance and outperforms conventional inter-sensor relationship modeling methods.
Overall, the concept of Guided-MELD and its application to the analysis of distributed multimedia sensor events provides valuable insights and practical implications. It showcases the importance of using multiple sensors and integrating data from various sources in order to effectively analyze events in complex real-world environments.