A database for fine grained activity detection of cooking activities

Rohrbach, Marcus; Amin, Sikandar; Andriluka, Mykhaylo; Schiele, Bernt

doi:10.1109/cvpr.2012.6247801

Cited by 455 publications

(445 citation statements)

References 41 publications

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“…The third dataset was a subset of MPII-Cooking (Rohrbach et al 2012). The subset contains 233 video clips with spatial resolution 1624 × 1224.…”

Section: Methodsmentioning

confidence: 99%

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Sentence Directed Video Object Codiscovery

Yu¹,

Siskind

2017

Int J Comput Vis

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Video object codiscovery can leverage the weak semantic constraint implied by sentences that describe the video content. Our codiscovery method, like other object codetection techniques, does not employ any pretrained object models or detectors. Unlike most prior work that focuses on codetecting large objects which are usually salient both in size and appearance, our method can discover small or medium sized objects as well as ones that may be occluded for part of the video. More importantly, our method can codiscover multiple object instances of different classes within a single video clip. Although the semantic information employed is usually simple and weak, it can greatly boost performance by constraining the hypothesized object locations. Experiments show promising results on three datasets: an average IoU score of 0.423 on a new dataset with 15 object

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“…The third dataset was a subset of MPII-Cooking (Rohrbach et al 2012). The subset contains 233 video clips with spatial resolution 1624 × 1224.…”

Section: Methodsmentioning

confidence: 99%

“…The subset contains 5 distinct object classes and 75 video clips with a total of 8854 frames. The last is a subset of MPII-Cooking (Rohrbach et al 2012), the most challenging dataset used in Gall (2014, 2017). The subset contains 7 distinct object classes and 233 video clips with a total of 70,259 frames.…”

Section: Figmentioning

confidence: 99%

Sentence Directed Video Object Codiscovery

Yu¹,

Siskind

2017

Int J Comput Vis

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“…First, we use DTs for which the state-of-the-art performance has been demonstrated on a dataset of cooking actions created by Rohrbach et al [7].…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

“…The present paper focuses on fine-grained action recognition including the recognition of similar actions for guitar picking. The use of dense trajectories (DTs) [5] is a state-of-the-art approach that can distinguish minute changes in activities, such as those of cooking and daily living [7]. The convolutional neural network [6], [8] has also been proposed for fine-grained action recognition.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Fine Motion Considering Individual Habit for Appearance-Based Proficiency Evaluation

Miyashita

Kataoka

Nakamura

2017

IEICE Trans. Inf. & Syst.

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SUMMARYWe propose an appearance-based proficiency evaluation methodology based on fine-motion analysis. We consider the effects of individual habit in evaluating proficiency and analyze the fine motion of guitar-picking. We first extract multiple features on a large number of dense trajectories of fine motion. To facilitate analysis, we then generate a histogram of motion features using a bag-of-words model and change the number of visual words as appropriate. To remove the effects of individual habit, we extract the common principal histogram elements corresponding to experts or beginners according to discrimination's contribution rates using random forests. We finally calculate the similarity of the histograms to evaluate the proficiency of a guitar-picking motion. By optimizing the number of visual words for proficiency evaluation, we demonstrate that our method distinguishes experts from beginners with an accuracy of about 86%. Moreover, we verify experimentally that our proposed methodology can evaluate proficiency while removing the effects of individual habit.

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“…Ziebart et al predicts people's future locations [34] and Kitani et al [12] forecasts human actions by considering the physical environment. Other works involving daily activities include daily action classification or summarization by egocentric videos [7,14,17], fall detection [15], and classification of cooking actions [11,21,23,26].…”

Section: Related Workmentioning

confidence: 99%

Snippet Based Trajectory Statistics Histograms for Assistive Technologies

İşcen

Wang

Duygulu

et al. 2015

Computer Vision - ECCV 2014 Workshops

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Abstract. Due to increasing hospital costs and traveling time, more and more patients decide to use medical devices at home without traveling to the hospital. However, these devices are not always very straight-forward for usage, and the recent reports show that there are many injuries and even deaths caused by the wrong use of these devices. Since human supervision during every usage is impractical, there is a need for computer vision systems that would recognize actions and detect if the patient has done something wrong. In this paper, we propose to use Snippet Based Trajectory Statistics Histograms descriptor to recognize actions in two medical device usage problems; inhaler device usage and infusion pump usage. Snippet Based Trajectory Statistics Histograms encodes the motion and position statistics of densely extracted trajectories from a video. Our experiments show that by using Snippet Based Trajectory Statistics Histograms technique, we improve the overall performance for both tasks. Additionally, this method does not require heavy computation, and is suitable for real-time systems.

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A database for fine grained activity detection of cooking activities

Cited by 455 publications

References 41 publications

Sentence Directed Video Object Codiscovery

Sentence Directed Video Object Codiscovery

Analyzing Fine Motion Considering Individual Habit for Appearance-Based Proficiency Evaluation

Snippet Based Trajectory Statistics Histograms for Assistive Technologies

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