Video understanding stands as a critical pillar of AI research, encompassing the multifaceted challenge of interpreting the rich visual and temporal information embedded in videos. Within this field, two key branches emerge: egocentric video understanding and exocentric video understanding, each posing unique challenges and opportunities.
Egocentric video understanding involves interpreting footage captured from a first-person perspective, offering a subjective view of the world as experienced by the wearer of the camera. This perspective provides invaluable insights into human activities, intentions, and interactions with the environment, enabling applications in areas like augmented reality, assistive technologies, and even psychological studies.
Exocentric video understanding, on the other hand, deals with analyzing footage captured from an external observer’s perspective. This viewpoint offers a more objective and comprehensive overview of scenes, enabling tasks such as object tracking, action recognition, and scene understanding. This has profound implications for fields like surveillance, robotics, and autonomous vehicles.
The alignment of egocentric and exocentric videos presents a fascinating avenue for research, aiming to bridge the gap between these two contrasting perspectives. By establishing correspondence between events, objects, and actions as observed from both viewpoints, AI systems can gain a deeper understanding of the world, enabling more sophisticated and context-aware applications. This alignment also holds the key to unlocking new possibilities for cross-view learning and knowledge transfer, where insights gained from one perspective can enrich the understanding of the other.
In essence, video understanding, encompassing both egocentric and exocentric perspectives and their alignment, represents a vibrant research frontier that promises to reshape the landscape of AI and pave the way for a new generation of intelligent systems capable of perceiving and interacting with the world more humanistically. Our research in this field involves national and international collaborations with the University of Catania, the Technical University of Munich, and big tech companies such as Huawei.