A Multi-viewpoint Outdoor Dataset for Human Action Recognition
Advancements in deep neural networks have contributed to near perfect results for many computer vision problems such as object recognition, face recognition and pose estimation. However, human action recognition is still far from human-level performance. Owing to the articulated nature of the human body, it is challenging to detect an action from multiple viewpoints, particularly from an aerial viewpoint. This is further compounded by a scarcity of datasets that cover multiple viewpoints of actions. To fill this gap and enable research in wider application areas, we present a multi-viewpoint outdoor action recognition dataset collected from YouTube and our own drone. The dataset consists of 20 dynamic human action classes, 2324 video clips and 503086 frames. All videos are cropped and resized to 720x720 without distorting the original aspect ratio of the human subjects in videos. This dataset should be useful to many research areas including action recognition, surveillance and situational awareness. We evaluated the dataset with a two-stream CNN architecture coupled with a recently proposed temporal pooling scheme called kernelized rank pooling that produces nonlinear feature subspace representations. The overall baseline action recognition accuracy is 74.0%.
Code (1)
Tasks
Action RecognitionFace RecognitionObject RecognitionPose EstimationTemporal Action LocalizationSimilar Papers 제목 키워드 기반
City-VLM: Towards Multidomain Perception Scene Understanding via Multimodal Incomplete Learning
Scene understanding enables intelligent agents to interpret and comprehend their environment. While existing large vision-language models (LVLMs) for scene understanding have primarily focused on indoor household tasks, …
Question AnsweringScene UnderstandingNeRF for Outdoor Scene Relighting
Photorealistic editing of outdoor scenes from photographs requires a profound understanding of the image formation process and an accurate estimation of the scene geometry, reflectance and illumination. A delicate manipu…
NeRFEmbodied Referring Expression Comprehension in Human-Robot Interaction
As robots enter human workspaces, there is a crucial need for them to comprehend embodied human instructions, enabling intuitive and fluent human-robot interaction (HRI). However, accurate comprehension is challenging du…
Referring ExpressionDistillation-guided Representation Learning for Unconstrained Gait Recognition
Gait recognition holds the promise of robustly identifying subjects based on walking patterns instead of appearance information. While previous approaches have performed well for curated indoor data, they tend to underpe…
Gait RecognitionRepresentation LearningSC-NeRF: Self-Correcting Neural Radiance Field with Sparse Views
In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the gene…
NeRFNovel View SynthesisSSIM