Human Routine Change Detection using Bayesian Modelling

Xu, Yangdi; Damen, Dima

doi:10.1109/icpr.2018.8545058

Cited by 4 publications

(2 citation statements)

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“…As opposed to deciding a task then collecting relevant data (or datasets), we opted for capturing natural interactions from which different tasks could be addressed. A few to-be-explored tasks on our dataset, that we are considering for future work are: dense captioning, skill understanding [34] and multi-day routine modelling [72].…”

Section: Discussionmentioning

confidence: 99%

The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines

Damen¹,

Doughty²,

Farinella³

et al. 2020

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Since its introduction in 2018, EPIC-KITCHENS has attracted attention as the largest egocentric video benchmark, offering a unique viewpoint on people's interaction with objects, their attention, and even intention. In this paper, we detail how this large-scale dataset was captured by 32 participants in their native kitchen environments, and densely annotated with actions and object interactions. Our videos depict nonscripted daily activities, as recording is started every time a participant entered their kitchen. Recording took place in 4 countries by participants belonging to 10 different nationalities, resulting in highly diverse kitchen habits and cooking styles. Our dataset features 55 hours of video consisting of 11.5M frames, which we densely labelled for a total of 39.6K action segments and 454.2K object bounding boxes. Our annotation is unique in that we had the participants narrate their own videos (after recording), thus reflecting true intention, and we crowd-sourced ground-truths based on these. We describe our object, action and anticipation challenges, and evaluate several baselines over two test splits, seen and unseen kitchens. We introduce new baselines that highlight the multimodal nature of the dataset and the importance of explicit temporal modelling to discriminate fine-grained actions (e.g. 'closing a tap' from 'opening' it up).

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Section: Discussionmentioning

confidence: 99%

The EPIC-KITCHENS Dataset: Collection, Challenges and Baselines

Damen¹,

Doughty²,

Farinella³

et al. 2020

Preprint

Self Cite

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“…It is an amalgam of a person's nature, face expressions, surroundings and physical gestures, according to Zhang et al [3] and Albu [4]. It is also defined as the state which is achieved after a series of events and actions [5]. Two types of behaviors can be categorized while studying different fields of society: routine behaviors and political behaviors [6,7].…”

Section: Introductionmentioning

confidence: 99%

Criminal Behavior Identification Using Social Media Forensics

et al. 2022

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Human needs consist of five levels, which are: physiological needs, safety needs, love needs, esteem needs and self-actualization. All these needs lead to human behavior. If the environment of a person is positive, healthy behavior is developed. However, if the environment of the person is not healthy, it can be reflected in his/her behavior. Machines are intelligent enough to mimic human intelligence by using machine learning and artificial intelligence techniques. In the modern era, people tend to post their everyday life events on social media in the form of comments, pictures, videos, etc. Therefore, social media is a significant way of knowing certain behaviors of people such as abusive, aggressive, frustrated and offensive behaviors. Behavior detection by crawling the social media profile of a person is a crucial and important idea. The challenge of behavior detection can be sorted out by applying social media forensics on social media profiles, which involves NLP and deep learning techniques. This paper is based on the study of state of the art work on behavior detection, and based on the research, a model is proposed for behavior detection. The proposed model outperformed with an F1 score of 87% in the unigram + bigram class, and in the bigram + trigram class, it gave an F1 score of 88% when compared with models applied on state of the art work. This study is a great benefit to cybercrime and cyber-security agencies in shortlisting the profiles containing certain behaviors to prevent crimes in the future.

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