CVPR2015 論文紹介

@jellied_unagi

今回紹介する論文

• 動作検出（Action detection / localization）

• 入力: 映像，出力: 特定動作を含む時空間oo

• 動作認識（Action recognition / classification）

• 入力: 映像，出力: 動作クラス

• 映像要約（Action summarization）

• 入力: （複数）映像，出力: 映像のうち重要な部分

2

Finding Action Tubes Georgia Gkioxari, Jitendra Malik http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Gkioxari_Finding_Action_Tubes_2015_CVPR_paper.html

Pipeline for generating action proposals (spatiotemporal volumes including actions)

3

Linking detection results via DP:

Actionness for Rt, Rt+1 + their spatial overlap

- Action detection (bounding boxes per frame) by learning CNN-based features with SVM

- Outlier suppression based on motion saliency

Fast Action Proposals for Human Action Detection and Search Gang Yu and Junsong Yuan http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Yu_Fast_Action_Proposals_2015_CVPR_paper.html

Pipeline for generating action proposals (spatiotemporal paths including actions)

4

- Human detection + dense trajectories to generateaction paths with an action-ness score

- Greedy sub-path search to find best path sets

Action-ness for box bt on path p(i)

Overlaps between paths (to avoid redundancy)

Overlaps between boxes (to ensure smoothness of paths)

Motion Part Regularization: Improving Action Recognition via Trajectory Group Selection Bingbing Ni, Pierre Moulin, Xiaokang Yang and Shuicheng Yan http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Ni_Motion_Part_Regularization_2015_CVPR_paper.html

Action localization (finding important motion parts) via group sparse optimization

5

Hierarchical clustering to generatetrajectory groups at various sizes

Finding discriminative trajectory groups viagroup lasso (sum of L2 regularizations)

Pooled Motion Features for First-Person Videos M. S. Ryoo, Brandon Rothrock, and Larry Matthies http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Ryoo_Pooled_Motion_Features_2015_CVPR_paper.html

Encoding temporal changes of features by various temporal pooling filters

6

Delving into Egocentric Actions Yin Li, Zhefan Ye and James M. Rehg http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Li_Delving_Into_Egocentric_2015_CVPR_paper.html

- Studying effective features for action recognition

- Better combinations: Obj. + Mot. + Ego-cues + Feat. around gaze

7

Modeling Video Evolution For Action Recognition Basura Fernando, Efstratios Gavves, Jose ́ Oramas M., Amir Ghodrati and Tinne Tuytelaars http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Fernando_Modeling_Video_Evolution_2015_CVPR_paper.html

Describe how videos change via learning-to-rank

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Order constraints allow non-linear scalings of temporal changes in actions

Gaze-enabled Egocentric Video Summarization via Constrained Submodular Maximization Jia Xu, Lopamudra Mukherjee, Yin Li, Jamieson Warner, James M. Rehg and Vikas Singh http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Xu_Gaze-Enabled_Egocentric_Video_2015_CVPR_paper.html

Single video summarization by using gaze information

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Segmenting shots where saccades occur

Measuring importance of shotsbased on fixation counts

R-CNN around PoG(to measure shot similarities)

Evaluation: Asking human experts to generate summaries

by grouping those action annotations, and asking them to select 5 ∼ 15 group of

consequent segments (referred to as events or blocks)

Video Co-summarization: Video Summarization by Visual Co-occurrence Wen-Sheng Chu, Yale Song and Alejandro Jaimes http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Chu_Video_Co-Summarization_Video_2015_CVPR_paper.html

Summarizing videos in a similar way to co-segmentation

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- Describing similarities between sub-shots by a bipartite graph

- Shots in a query video x shots in relevant videos

- Maximum cliques in the graph describe relevant events

EgoSampling: Fast-Forward and Stereo for Egocentric Videos Yair Poleg, Tavi Halperin, Chetan Arora and Shmuel Peleg http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Poleg_EgoSampling_Fast-Forward_and_2015_CVPR_paper.html

Frame sampling strategy to stabilize and fast-forward first-person videos

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Finding a shortest path where selected frames have 1) FOE at their center 2) faster camera motions 3) similar appearances to their previous / next frames