Recognizing Activities with Multiple Cues

Biswas, Rahul; Thrun, Sebastian; Fujimura, Kikuo

doi:10.1007/978-3-540-75703-0_18

Cited by 30 publications

(24 citation statements)

References 27 publications

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“…Several approaches have been proposed in the literature for long-term activity recognition - [3,5,7,7,8] are but a few recent examples. To address the issue of uncertainty mentioned above, various probabilistic reasoning frameworks have been adopted.…”

Section: A Representative Approachmentioning

confidence: 99%

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On Complex Event Processing for Real-Time Situational Awareness

Stojanović

Artikis

2011

Rule-Based Reasoning, Programming, and Applications

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Abstract. In this paper we give an overview of the existing research results and open research challenges in applying complex event processing for real-time situational awareness. We consider two different viewpoints: better detection of emerging complex situations and prediction of future situations. In order to illustrate these viewpoints we consider two application areas: activity recognition from the video content and social media observation, respectively.

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Section: A Representative Approachmentioning

confidence: 99%

“…MLN have been recently used for long-term activity recognition -consider, for example, [3,8]. (A detailed account of the use of MLN for activity recognition and complex event processing in general may be found in [1].)…”

Section: A Representative Approachmentioning

confidence: 99%

On Complex Event Processing for Real-Time Situational Awareness

Stojanović

Artikis

2011

Rule-Based Reasoning, Programming, and Applications

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“…A related approach is that by Biswas et al (2007), who employs dynamic Markov Logic to represent stochastic relational processes.…”

Section: Related Workmentioning

confidence: 99%

Stochastic relational processes: Efficient inference and applications

2010

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One of the goals of artificial intelligence is to develop agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. While standard probabilistic sequence models provide efficient inference and learning techniques for sequential data, they typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient to cope with complex sequential data. In this paper, we introduce a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic (Causal Probabilistic Logic), an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions and employing a Markov assumption, inference and learning become more tractable and effective. Specifically, we show how to solve part of the inference and learning problems directly at the first-order level, while transforming the remaining part into the problem of computing all satisfying assignments for a Boolean formula in a binary decision diagram.We experimentally validate that the resulting technique is able to handle probabilistic relational domains with a substantial number of objects and relations.

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“…A first-order probabilistic model that combines multiple clues to classify human activities from video data is introduced in (Biswas, Thrun, & Fujimura, 2007). The probabilistic model is implemented as a Dynamic Markov Logic Network that groups fifteen FOL propositions.…”

Section: Traditional Approaches In Activity Recognitionmentioning

confidence: 99%

Modeling and Inference with Relational Dynamic Bayesian Networks

Manfredotti

2009

Lecture Notes in Computer Science

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AcknowledgementsPrima di partire per un lungo viaggio devi portare con te la voglia di non tornare piu'.Irene Grandi from the song "Prima di partire per un lungo viaggio".Molti (maligni!) penseranno che la citazione si riferisce al mio non voler mai piu' tornare in Bicocca ... Ebbene no! Il dottorato e' stato un viaggio e come e' giusto, in quanto viaggio, l'ho percorso "gustandomelo".Il dottorato e' stata un'esperienza molto costruttiva e la crescita personale e di ricercatore che ne ho derivato non e' stata tutta merito mio ma, soprattutto, delle persone che mi hanno indirizzato e accompagnato in questo cammino e che ora mi piacerebbe ringraziare una per una.Prima tra tutte la mia relatrice la professoressa Messina ("La Prof"). Ha saputo consigliarmi sulla strada da prendere e, quando mi perdevo, ha saputo sempre guidarmi verso il raggiungimento dei miei obiettivi. La collaborazione con lei e' stata un'importante occasione di crescita per me."A braccetto" con La Prof non puo' mancare il professor Archetti. Sempre presente anche se spesso non fisicamente. Tante delle idee presenti in questa tesi sono nate dalla discussione con lui, da sue proposte e dalla sua voglia di "provare".Nel professor Sorrenti ho sempre trovato un confidente e un amico, ma anche una persona sul cui giudizio potevo contare e della cui opinione potevo fidarmi. Grazie! I tre anni di dottorato sono stati tre anni pieni di amici incontrati lungo questo cammino: Dario, Yuri (che ha consegnato la mia domanda di tesi, grazie!) Simone, Sergio, i due Daniele (che poi ho ritrovato a Toronto), Sara (Un amico e' per sempre!), Elena, Daniele (che per primo mi ha spiegato particle filter), Axel (che ora iniziera' questo "viaggio"), Paolo, Davide e tanti altri con i quali ho condiviso tanti momenti di scherzi, di pausa, di chiacchera e anche a volte di ..., grazie a tutti, ragazzi! Ma piu' di tutti devo ringraziare le mie due colleghe e compagne di (s)ventura: Elisabetta e Ilaria. Ne abbiamo passate tante insieme, lavorativamente parlando e non solo, ma i miei ringraziamenti sono soprattutto per questo ultimo anno e mezzo in cui, se pur lontana, mi avete sempre e comunque fatto sentire una di voi. Grazie! Il MIND (ex LIFE) e' stato un bellissimo gruppo nel quale lavorare (e per ognuno degli studenti passati di li' conservo un ricordo particolare) ma c'e' un altro gruppo che mi ha accolta e fatta sentire a casa in quest ultimo periodo: COGS alla UofT. Per farmi capire anche da loro cambio lingua per un attimo. IVAt UofT I found a great professor, Craig, who always gave me the right advice about the right thing to do and, more importantly, who introduced me to David, whose ideas and suggestions (during our long meetings) arrived at the right moment, a turning point for this work.But I also found a lot of colleagues and friends always there when I need something. I remember the long lunches with Laurent, that made me feeling like at home, the small talks with Kevin who supported me a lot, the "strange" conversations with Tyler and the support of Mark and ...

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Recognizing Activities with Multiple Cues

Cited by 30 publications

References 27 publications

On Complex Event Processing for Real-Time Situational Awareness

On Complex Event Processing for Real-Time Situational Awareness

Stochastic relational processes: Efficient inference and applications

Modeling and Inference with Relational Dynamic Bayesian Networks

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