Recognizing human action from a far field of view

Chen, Chia-Chih; Aggarwal, J. K.

doi:10.1109/wmvc.2009.5399231

Cited by 47 publications

(45 citation statements)

References 20 publications

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“…We tabulate the average accuracy per activity as well as the overall accuracy of each team and the baseline method in Table 5. Note that prior to this competition, Chen and Aggarwal [2] have tested their method on part of this dataset (60 sequences of the lawn scene). They were able to achieve 100% accuracy on the partial dataset.…”

Section: Bu Action Covariance Manifoldsmentioning

confidence: 99%

An Overview of Contest on Semantic Description of Human Activities (SDHA) 2010

Ryoo

Chen²,

Aggarwal³

et al. 2010

Lecture Notes in Computer Science

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210

153

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Abstract. This paper summarizes results of the 1st Contest on Semantic Description of Human Activities (SDHA), in conjunction with ICPR 2010. SDHA 2010 consists of three types of challenges, High-level Human Interaction Recognition Challenge, Aerial View Activity Classification Challenge, and Wide-Area Activity Search and Recognition Challenge. The challenges are designed to encourage participants to test existing methodologies and develop new approaches for complex human activity recognition scenarios in realistic environments. We introduce three new public datasets through these challenges, and discuss results of state-ofthe-art activity recognition systems designed and implemented by the contestants. A methodology using a spatio-temporal voting [19] successfully classified segmented videos in the UT-Interaction datasets, but had a difficulty correctly localizing activities from continuous videos. Both the method using local features [10] and the HMM based method [18] recognized actions from low-resolution videos (i.e. UT-Tower dataset) successfully. We compare their results in this paper.

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Section: Bu Action Covariance Manifoldsmentioning

confidence: 99%

An Overview of Contest on Semantic Description of Human Activities (SDHA) 2010

Ryoo

Chen²,

Aggarwal³

et al. 2010

Lecture Notes in Computer Science

Self Cite

210

153

View full text Add to dashboard Cite

show abstract

“…There are only a few works in literature that specifically address the impact of low-video quality on action recognition performance; all are limited to only certain factors influencing video quality or done rather ad hocly [15,16,[33][34][35].…”

Section: Action Recognition In Low-quality Videosmentioning

confidence: 99%

“…Chen and Aggarwal [33] proposed to use supervised PCA projected time series of histogram of oriented gradients (HOG) and histogram of oriented optical flow (HOOF) descriptor, to encode pose and motion information of action videos from a far distance. Their work focused on recognizing human actions from a far field of view where the size of humans is typically not more than 40 pixels.…”

Section: Action Recognition In Low-quality Videosmentioning

confidence: 99%

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Exploiting textures for better action recognition in low-quality videos

Rahman

See

2017

J Image Video Proc.

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Human action recognition is an increasingly matured field of study in the recent years, owing to its widespread use in various applications. A number of related research problems, such as feature representations, human pose and body parts detection, and scene/object context, are being actively studied. However, the general problem of video quality-a realistic issue in the face of low-cost surveillance infrastructure and mobile devices, has not been systematically investigated from various aspects. In this paper, we address the problem of action recognition in low-quality videos from a myriad of perspectives: spatial and temporal downsampling, video compression, and the presence of motion blurring and compression artifacts. To increase the resilience of feature representation in these type of videos, we propose to use textural features to complement classical shape and motion features. Extensive results were carried out on low-quality versions of three publicly available datasets: KTH, UCF-YouTube, HMDB. Experimental results and analysis suggest that leveraging textural features can significantly improve action recognition performance under low video quality conditions.

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“…Apart from various human detection, HOG descriptor is also employed for gesture recognition by [12]- [15] due to its robust performance. The HOG descriptor can provide details of human gestures.…”

Section: Hog Descriptormentioning

confidence: 99%

Gesture Recognition from One Example Using Depth Images

Liang¹,

Zheng²

2013

LNSE

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Abstract-By using depth images, this paper presents an approach capable of recognizing the gesture from only one example of each class. Background removal and denoising are performed on depth images firstly. Motion Energy Information (MEI) images are then obtained through calculating the differences between consecutive frames. Within each MEI image, we represent successive movements by time series using Histograms of Oriented Gradients (HOG) descriptor. Principle Component Analysis (PCA) reconstruction approach is applied on the descriptor to find a set of discriminantly informative principle components (PCs) from the corresponding training gesture. Next the descriptors extracted from test gestures are reconstructed back utilizing each set of PCs from training gestures. Finally the test gestures are recognized according to the set of PCs which produces the lowest reconstruction error. We evaluate our approach on the task of recognizing gestures from one example using depth images, and compare the performance of our approach with other methods, reaching a promising result.

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Recognizing human action from a far field of view

Abstract: In this paper, we present a novel descriptor to characterize

Cited by 47 publications

References 20 publications

An Overview of Contest on Semantic Description of Human Activities (SDHA) 2010

An Overview of Contest on Semantic Description of Human Activities (SDHA) 2010

Exploiting textures for better action recognition in low-quality videos

Gesture Recognition from One Example Using Depth Images

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