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Predicting Important Objects for Egocentric Video Summarization

Yong Jae Lee, Kristen Grauman

arXiv:1505.04803v1cs.CV

TL;DR

The paper addresses how to summarize hours of egocentric video around the people and objects that matter to the camera wearer. It predicts category-independent region importance from egocentric cues, detects temporal events, and selects storyboard frames under importance or length controls. Experiments report significantly more informative summaries than prior methods.

  • Problem

    Traditional summarization methods may rely on low-level appearance or motion cues and fail to identify which objects matter in changing egocentric scenes.

  • Method

    The method learns category-independent importance from egocentric region cues, predicts important regions, partitions video into events, and selects storyboard frames using importance or length criteria.

  • Results

    The approach produces significantly more informative summaries than prior methods on challenging real-world egocentric videos.

  • Takeaways & Limitations

    Object-driven importance can focus compact wearable-camera summaries on key people and objects while ignoring irrelevant or redundant information.

  • Takeaways & Limitations

    The importance-criterion strategy does not provide direct control over summary length, whereas the budgeted strategy does not impose an absolute importance threshold.

Abstract

from arXiv · show

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.

1 Introduction

The paper targets compact egocentric summaries that preserve important people, objects, and interactions rather than irrelevant or redundant regions. It learns category-independent importance cues and selects storyboard frames using temporal events and either importance or length constraints.

  • Motivation: Egocentric summaries must handle changing backgrounds and high-level object relevance, which low-level keyframe methods may miss.Existing approaches can select irrelevant frames or regions when they lack information about which objects matter.
  • Motivation: The method exploits wearable-camera properties, including visible hands, first-person activities, social interactions, and user interests.
  • Method: A regression model combines egocentric and low-level region cues, predicts important regions, partitions video into temporal events, and selects representative storyboard instances.Cues include frequency, proximity to the wearer’s hand, object-like appearance and motion, size, width, and height.
  • Method: The summary’s compactness can be controlled by an importance criterion or a target length using an energy function optimized with dynamic programming.The budgeted formulation accounts for frame importance, visual dissimilarity, and temporal displacement.
  • Core idea: The approach learns category-independent importance so an object’s relevance can vary with its role and context, including for previously unseen objects.The same person or object category may be important in one situation and unimportant in another.
  • Results: Experiments on uncontrolled real-world egocentric videos totaling 27 hours report better saliency estimates and more informative summaries than traditional methods.

2 Related Work

Prior video summarization and saliency methods rely heavily on low-level cues, static-camera assumptions, short clips, or human guidance. This work instead summarizes egocentric video by predicting important objects.

  • Video summarization: Static keyframe methods use optical flow or global color and texture differences, which can select irrelevant frames in continuous egocentric video.
  • Video summarization: Montage methods often assume short clips with few objects or require a human to select frames and key points.
  • Video summarization: Compact dynamic summaries assume a static camera, while retargeting methods do not model differing object importance.
  • Saliency detection: Saliency research has progressed from bottom-up image cues toward learned high-level measures, but this paper targets saliency for summarization rather than visual search.
  • Egocentric visual analysis: Egocentric analysis has addressed events, recognition, handled objects, novelty, hands, gaze, and social interaction, but visual summarization by predicting important objects was identified as new here.

3 Approach

The approach defines importance through the camera wearer’s interactions and predicts important regions with category-independent egocentric cues. It then segments the video into events and selects representative keyframes for a storyboard.

  • Problem formulation: Importance is defined around people and objects with which the camera wearer has significant interaction.
  • Importance prediction: A category-independent regression model predicts whether an image region belongs to an important person or object.
  • Pipeline: The pipeline uses egocentric saliency cues, partitions the video into temporal events, scores regions, and selects representative storyboard frames.
  • Annotations: The study collects important-person and important-object annotations through Mechanical Turk text descriptions, polygon annotations, and example labeled regions.

3.1 Egocentric video data collection

The UT Ego dataset contains natural daily-life videos recorded by four subjects using a wearable camera. The collection uses long recordings from uncontrolled activities to support category-independent evaluation.

  • Capture setup: The Looxcie camera records at 15 fps and 320 x 480 resolution while worn near eye level around the ear.
  • Dataset: Ten videos from four subjects, each three to five hours long, form the 37-hour UT Egocentric dataset.
  • Participants: Subjects included an undergraduate, two graduate students, and an office worker of both genders in their early-to-late twenties.
  • Collection protocol: Participants recorded natural daily activities without staging, providing varied routines for validating category-independence.

3.2 Annotating important regions in video

The paper constructs training annotations that capture which people and objects are essential to summarizing egocentric video. A two-step process separates free-form description from region marking to reduce annotation bias.

  • Egocentric video provides cues such as visible hands and centered objects that constrain judgments of importance.
  • Workers first watch an accelerated video and describe essential people or objects, then mark corresponding regions in sampled frames.
  • The two-step procedure was more effective than asking one worker to watch the video and annotate regions simultaneously.
  • Separating description from annotation reduces pressure to choose objects that are easier to outline rather than objects judged most important.

3.3 Learning egocentric region importance

The method predicts region importance with a category-independent regressor using egocentric, object-based, and geometric cues. These cues include interaction and gaze proxies, recurrence, object-likeness, motion, face overlap, and region geometry.

  • Candidate regions are generated per frame, then described with cues targeting interaction, gaze, recurrence, object-like appearance, motion, and human likelihood.
  • Hand interaction is approximated by centroid distance to the nearest detected hand, while gaze is approximated by centroid distance to the frame center.
  • Frequency features count repeated region or SIFT-point matches across surrounding frames, with higher values indicating more frequent regions.
  • Object cues distinguish complete object segments using object-likeness, relative motion, and overlap with detected frontal faces.
  • The feature representation contains 14 dimensions, combining 4 egocentric, 3 object, and 7 region features.
  • A linear regressor with pair-wise feature interactions predicts continuous importance scores from standardized features and ground-truth overlap.

3.4 Segmenting the video into temporal events

The video is divided into temporal events by clustering frames using global color similarity while penalizing temporal separation. This avoids the oversegmentation caused by conventional shot detection in wearable-camera footage.

  • Events allow the summary to treat appearances of the same object or person in different contexts as separate instances.
  • The method replaces shot-boundary detection because frequent head movements can oversegment egocentric events.
  • A pairwise frame-distance matrix measures global color dissimilarity and includes a temporal-window weight that discourages grouping distant frames.
  • Complete-link agglomerative clustering forms events until the smallest maximum interframe distance exceeds the stopping threshold, with cluster endpoints defining event boundaries.

3.5 Discovering an event’s key people/objects

Within each temporal event, the method groups region instances likely to belong to the same object and selects representative regions while removing redundancy.

  • The event-level objective is to select important people and objects while avoiding redundant instances across frames.
  • Each bottom-up region is scored by the importance regressor, and candidate clusters are generated because the number of important objects is unknown.
  • An affinity matrix based on color similarity is factorized into tight, possibly overlapping inlier/outlier clusters representing candidate object instances.
  • Clusters are ranked by average member importance, redundant clusters are removed using affinity to higher-ranked clusters, and the highest-scoring region represents each remaining cluster.

3.6 Generating a storyboard summary

The method builds storyboard summaries by selecting important people and objects under either an importance threshold or a fixed length budget. Budgeted selection balances importance, visual uniqueness, and temporal coverage, while the two strategies trade off compactness against direct length or importance control.

  • Importance criterion: The system groups above-threshold regions belonging to the same person or object and selects the highest-scoring frame from each group.This produces the most compact summary containing unique instances that meet the importance criterion.
  • Length budget: A length-budgeted summary selects k frames to satisfy requests such as a five-minute storyboard.The selected frames form a temporally ordered subsequence of the original video.
  • Length budget: The budgeted energy function rewards important frames, visual uniqueness, and temporal dispersion across the video.Frame importance uses the highest-scoring region; color-histogram distances discourage redundancy, while frame-index differences spread selections over time.
  • Length budget: Dynamic programming efficiently finds the optimal k-frame set by exploiting the problem’s optimal substructure.The minimum energy can be computed in O(F 2k) time and recovered by backtracking.
  • Trade-offs: The importance-criterion strategy controls which objects qualify but not output length, whereas the budgeted strategy controls length but not an absolute importance threshold.Both strategies produce storyboard summaries displaying selected important people and objects.
  • Storyboard use: The resulting storyboard also serves as a visual index for revisiting specific video moments or searching for less important co-occurring objects and people.The method’s pipeline includes temporal event segmentation, region scoring, grouping, and storyboard generation.

4 Results

Across two egocentric datasets, the method was evaluated for important-region prediction and object-driven summarization, including cross-user testing, ablations, and comparisons with saliency and keyframe baselines. Results show stronger identification and presentation of important objects, while revealing failure cases tied to segmentation, hands, background frequency, and feature interactions.

  • Evaluation setup: The evaluation used UT Ego and ADL, with comparisons against saliency and summarization methods plus a user study involving over 25 subjects.UT Ego contains 17 hours and ADL 10 hours of egocentric video; UT Ego splits separate training and test camera wearers.
  • Important-region prediction: The proposed predictor significantly outperformed three existing methods for important-region prediction by distinguishing wearer-important objects from merely object-like regions.The baselines included two high-level object-like scores and one bottom-up saliency detector.
  • Failure cases: Observed failures included under-segmentation, incorrectly important hands, and background regions made salient by high frequency.The method could also favor combined regions with higher object-like scores than the target object alone.
  • Ablation: Pairwise feature terms improved expressiveness beyond the original features: linear regression reached average precision 0.20, while L1-regularized regression ranged from 0.14-0.20.The ablation concludes that pairwise terms are necessary to better capture relationships between features and importance values.
  • Cue analysis: Region size received the highest learned weight, while gaze, frequency, and frontal-face overlap were among the strongest egocentric cues.The results interpret these weights as reflecting distance, attention, and interaction with the camera wearer.
  • Cue analysis: Face-overlap and y-position interactions were more informative than face overlap alone, while object-like appearance and y-position also received high weight.These interactions suggest that important people and objects often appear at characteristic heights in the egocentric frame.
  • Summarization results: For User 2, the method found 62% of important objects in 27 frames, versus 54% for uniform keyframes and 46% for adaptive event-based sampling.Across summaries, the method also averaged 9.13 events per video and 2.05 people or objects per event.
  • Summarization results: The method selected more prominent views of important objects and produced summaries with better recall and focus than keyframe baselines.The prominence analysis used centroid distance to the frame center; baselines were prone to unimportant or redundant frames.

4.5 Budgeted frame selection accuracy

The budgeted frame-selection approach constructs compact summaries by selecting frames that cover important objects while accounting for event structure and redundancy. Compared with generic baselines, it produces more informative summaries at a given frame budget.

  • Evaluation setup: The method evaluates 10-frame-style requests by plotting important-object recall against summary length.Each curve point represents a summary with a different required number of frames.
  • Baselines: The approach compares against multi-document summarization, dissimilarity-based keyframe selection, no-events selection, and uniform sampling.The baselines isolate generic summarization strategies and the effects of importance prediction, event segmentation, and region grouping.
  • Results: Our model significantly outperforms keyframe selection because importance modeling avoids alternating redundant frames containing the same visual elements.Region clustering represents each object in each event with a single region/frame.
  • Results: Our model outperforms multi-document summarization on all but one user, whose low-level image cues can select non-essential frames.The multi-document method still selects diverse content throughout the video.
  • Results: With longer summaries, the method consistently outperforms uniform sampling because uniform sampling ignores object importance and repeats important objects.Uniform sampling performs similarly for very short summaries, when temporal spread can capture unique people or objects.
  • Results: The method also outperforms no-events selection, which can choose the same important object multiple times without event segmentation and region grouping.Example summaries show the proposed method focusing on important people and objects.

4.6 User studies to evaluate summaries

User studies compare the proposed summaries with uniform sampling and other baselines using camera-wearer judgments and independent viewers. Across UT Ego and ADL, viewers generally preferred the proposed summaries and judged them closer to the day’s text-described story, although simple days reduced the advantage.

  • Study design: The study uses camera wearers and 25 uninvolved subjects to assess summary quality from firsthand and independent perspectives.Camera wearers compare summaries directly, while uninvolved subjects judge whether events are understandable without firsthand experience.
  • Study design: Subjects compare summaries on important people and objects, overall summary quality, and correspondence with a camera-wearer text summary.The first question isolates prominent-object coverage, while the second addresses the overall story.
  • UT Ego results: 68.75% of 16 camera-wearer comparisons preferred the proposed summaries over uniform sampling.Uniform sampling was sufficient for one simple laptop-focused day, limiting the proposed method’s advantage in that case.
  • UT Ego results: On UT Ego, the proposed summaries scored 2.61 ± 0.97 for following text summaries, compared with 3.43 ± 1.05 for keyframes and 3.2 for multi-document summarization.Scores range from 1, very well, to 5, very poorly.
  • UT Ego results: Across UT Ego baseline comparisons, the proposed summaries were preferred 48–62% of the time and judged worse 24–38% of the time.These comparisons used 25 subjects and 16 comparisons against each baseline.
  • ADL results: On ADL, the proposed summaries scored 2.71 ± 1.02 against 3.58 ± 0.97 for keyframes, 2.99 ± 1.15 for multi-document summarization, and 2.89 ± 1.06 for uniform sampling.The proposed method showed clear improvement over keyframes, while gains over the other baselines were smaller than on UT Ego.

5 Conclusion and Future Work

The paper concludes that predicting important regions with egocentric cues enables more informative wearable-video summaries, with both importance-based and fixed-length controls. It also identifies subjectivity and imperfect event segmentation as important boundaries for the current approach.

  • Conclusion: The method predicts important regions with egocentric cues and supports both importance-based and length-budgeted summary compactness.The fixed-length setting uses an efficient optimization strategy to recover the best k-frame summary.
  • Conclusion: Experiments show significantly more informative summaries than prior methods.The conclusion attributes this result to accounting for key objects driving first-person interactions.
  • Future work: Importance cues can be shared across users, but subjective judgments about which people matter may require a wearer-specific model.The proposed wearer-independent model could be complemented by training on wearer input.
  • Future work: Imperfect event segmentation can produce redundant keyframes showing the same object in wearable video.The paper suggests combining scene appearance with GPS information to improve event separation.
  • Future work: Audio, accelerometer, and GPS signals could add speech, activity, gesture, and location context to importance prediction.The paper gives context-dependent examples such as plates in restaurants and athletes in stadiums.
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