Recognizing Activities via Bag of Words for Attribute Dynamics

Li, Weixin; Yu, Qian; Sawhney, Harpreet; Vasconcelos, Nuno

doi:10.1109/cvpr.2013.334

Cited by 41 publications

(39 citation statements)

References 27 publications

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“…In our opinion, the Weizman dataset is already solved since many researchers report accuracies of 100%. However, in recent publications [11,22,30] this dataset is still used to evaluate the corresponding methods. In order to allow a comparison to recent works and to show the benefit of our proposed method we evaluate VLBPs on this dataset too.…”

Section: Resultsmentioning

confidence: 99%

Computation strategies for volume local binary patterns applied to action recognition

Baumann

Ehlers

Rosenhahn

et al. 2014

2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Volume Local Binary Patterns are a well-known feature type to describe object characteristics in the spatiotemporal domain. Apart from the computation of a binary pattern further steps are required to create a discriminative feature. In this paper we propose different computation methods for Volume Local Binary Patterns. These methods are evaluated in detail and the best strategy is shown. A Random Forest is used to find discriminative patterns. The proposed methods are applied to the well-known and publicly available KTH dataset and Weizman dataset for singleview action recognition and to the IXMAS dataset for multiview action recognition. Furthermore, a comparison of the proposed framework to state-of-the-art methods is given.

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Section: Resultsmentioning

confidence: 99%

Computation strategies for volume local binary patterns applied to action recognition

Baumann

Ehlers

Rosenhahn

et al. 2014

2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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show abstract

“…The time series recognition methods investigated are summarized in Table 2. Whether they utilise Fisher Vector √ 16 [11], 15 [17] 85 [11], 60 [17] Dynamic System √ 25 [7], 5 [18] 93( [7], [18])…”

Section: Methodsmentioning

confidence: 99%

What are the Limits to Time Series Based Recognition of Semantic Concepts?

Wang

Sun

Smeaton

2016

MultiMedia Modeling

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Abstract. Most concept recognition in visual multimedia is based on relatively simple concepts, things which are present in the image or video. These usually correspond to objects which can be identified in images or individual frames. Yet there is also a need to recognise semantic concepts which have a temporal aspect corresponding to activities or complex events. These require some form of time series for recognition and also require some individual concepts to be detected so as to utilise their time-varying features, such as co-occurrence and re-occurrence patterns. While results are reported in the literature of using concept detections which are relatively specific and static, there are research questions which remain unanswered. What concept detection accuracies are satisfactory for time series recognition? Can recognition methods perform equally well across various concept detection performances? What affecting factors need to be taken into account when building concept-based high-level event/activity recognitions? In this paper, we conducted experiments to investigate these questions. Results show that though improving concept detection accuracies can enhance the recognition of time series based concepts, they do not need to be very accurate in order to characterize the dynamic evolution of time series if appropriate methods are used. Experimental results also point out the importance of concept selection for time series recognition, which is usually ignored in the current literature.

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“…Bag of Words [17] pipeline is used here for the classification of videos. Various other feature coding approaches have been proposed in addition to the bag of features, like the Fisher Vectors [6], vector of locally aggregated descriptors [7] and the super vectors [8]. In [9], [10], [11] the effective local volume detectors for the feature learning, designing is considered.…”

Section: Bag Of Featuresmentioning

confidence: 99%