Papers › Contextual Action Cues from Camera Sensor for Multi-Stream Action Recognition

Contextual Action Cues from Camera Sensor for Multi-Stream Action Recognition

20 Mar 2019Sensors 2019, 19(6), 1382 2019 3archive 2025-07-28

Jongkwang Hong, Bora Cho, Yong Won Hong, Hyeran Byun

In action recognition research, two primary types of information are appearance and motion information that is learned from RGB images through visual sensors. However, depending on the action characteristics, contextual information, such as the existence of specific objects or globally-shared information in the image, becomes vital information to define the action. For example, the existence of the ball is vital information distinguishing “kicking” from “running”. Furthermore, some actions share typical global abstract poses, which can be used as a key to classify actions. Based on these observations, we propose the multi-stream network model, which incorporates spatial, temporal, and contextual cues in the image for action recognition. We experimented on the proposed method using C3D or inflated 3D ConvNet (I3D) as a backbone network, regarding two different action recognition datasets. As a result, we observed overall improvement in accuracy, demonstrating the effectiveness of our proposed method.

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Tasks

Action Recognition

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Action Recognition HMDB-51 Multi-stream I3D Average accuracy of 3 splits 80.92 #18 of 77 Archive leaderboard report
Action Recognition UCF101 Multi-stream I3D 3-fold Accuracy 97.2 #21 of 91 Archive leaderboard report

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