Predicting Important Objects for Egocentric Video Summarization (1505.04803v1)

Abstract: We present a video summarization approach for egocentric or "wearable" camera data. Given hours of video, the proposed method produces a compact storyboard summary of the camera wearer's day. In contrast to traditional keyframe selection techniques, the resulting summary focuses on the most important objects and people with which the camera wearer interacts. To accomplish this, we develop region cues indicative of high-level saliency in egocentric video---such as the nearness to hands, gaze, and frequency of occurrence---and learn a regressor to predict the relative importance of any new region based on these cues. Using these predictions and a simple form of temporal event detection, our method selects frames for the storyboard that reflect the key object-driven happenings. We adjust the compactness of the final summary given either an importance selection criterion or a length budget; for the latter, we design an efficient dynamic programming solution that accounts for importance, visual uniqueness, and temporal displacement. Critically, the approach is neither camera-wearer-specific nor object-specific; that means the learned importance metric need not be trained for a given user or context, and it can predict the importance of objects and people that have never been seen previously. Our results on two egocentric video datasets show the method's promise relative to existing techniques for saliency and summarization.

• The paper introduces a novel method for egocentric video summarization that identifies important objects and people by leveraging unique first-person cues like gaze and hand proximity.
• It uses a linear regression model trained with these egocentric cues and general object features to predict the relative importance of regions in the video.
• Evaluations on datasets and user studies show this approach significantly outperforms traditional methods, demonstrating its effectiveness in identifying important objects.

Predicting Important Objects for Egocentric Video Summarization

The paper "Predicting Important Objects for Egocentric Video Summarization" by Yong Jae Lee and Kristen Grauman presents a novel approach to video summarization, specifically tailored for egocentric—or wearable—camera data. Unlike traditional keyframe selection techniques that rely on low-level appearance and motion cues, the authors introduce a method that centers on identifying the most important objects and people with whom the camera wearer interacts.

Egocentric video data offers a unique first-person perspective that is distinct from environmental cameras, capturing the user's activities and interactions directly. The authors leverage this aspect by defining region cues that denote high-level saliency, such as proximity to hands, the wearer's gaze, and frequency of occurrence in the video. These cues allow them to train a regressor capable of predicting the relative importance of new regions, independent of both specific camera wearers and objects.

Key components of their approach include:

1. Egocentric Importance Features: They focus on developing cues that highlight interaction, gaze, and frequency, all integral to egocentric data, alongside general object features like motion and appearance saliency.
2. Prediction Model: Using a linear regression model with interaction terms, the authors capture significant signals about a region's importance, informed by the novel egocentric cues.
3. Temporal Event Detection: The approach segments the continuous video into discrete events, aiding in both the eventual summarization and contextual grouping of interactions.
4. Summary Generation: They offer two summarization techniques—one based on an importance criterion and another driven by a target summary length using dynamic programming. This allows for scalability and ensures summaries remain focused on key happenings.

The implications of this research are multifaceted. Practically, the ability to generate concise, insightful summaries of extensive egocentric video footage is valuable for applications ranging from wearable video diaries to surveillance analysis and beyond. Theoretically, the paper contributes to understanding how egocentric features can be harnessed for object saliency predictions, potentially informing future developments in robotics where first-person summaries are crucial.

This paper also opens avenues for further research, such as customization for specific user contexts or integrating multimodal data to refine event segmentation. Additionally, enhancing user-specific models could address the inherent subjectivity in determining what constitutes 'importance' in a user's daily interactions.

Evaluations on the UT Ego dataset, along with user studies, indicate that this approach outperformed traditional techniques and demonstrated a higher recall rate for important objects, validating the effectiveness of their egocentric feature set and prediction model. The exploration on the ADL dataset also showcased its adaptability to different types of egocentric videos.

In conclusion, this research provides a significant step forward in generating meaningful summaries from wearable camera data, presenting a detailed exploration of the relationship between egocentric cues and perceived importance in video summarization.