Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition

Rodríguez, Mikel; Javed, Ali; Shah, Mubarak

doi:10.1109/cvpr.2008.4587727

Cited by 998 publications

(786 citation statements)

References 19 publications

(19 reference statements)

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“…Recently, more challenging datasets were constructed by collecting realistic videos from movies [13,12,14]. These movie scenes are taken from varying view points with complex backgrounds, in contrast of the previous public datasets [16,8].…”

Section: Previous Datasetsmentioning

confidence: 99%

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

Ryoo

Chen²,

Aggarwal³

et al. 2010

Recognizing Patterns in Signals, Speech, Images and Videos

199

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: Previous Datasetsmentioning

confidence: 99%

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

Ryoo

Chen²,

Aggarwal³

et al. 2010

Recognizing Patterns in Signals, Speech, Images and Videos

199

153

View full text Add to dashboard Cite

show abstract

“…However, this approach is only capable of classifying, rather than detecting, activities. Other approaches include filtering techniques [29] and sampling of video patches [1]. Hierarchical techniques for activity recognition have been used as well, but these typically focus on neurologically-inspired visual cortex-type models [9,32,23,28].…”

Section: Introductionmentioning

confidence: 99%

Unstructured human activity detection from RGBD images

Sung

Ponce

Selman

et al. 2012

2012 IEEE International Conference on Robotics and Automation

174

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Abstract-Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics. In this paper, we perform detection and recognition of unstructured human activity in unstructured environments. We use a RGBD sensor (Microsoft Kinect) as the input sensor, and compute a set of features based on human pose and motion, as well as based on image and pointcloud information. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM), which considers a person's activity as composed of a set of sub-activities. We infer the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve good performance even when the person was not seen before in the training set. 1

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“…The most notable recent foray into sports footage in the literature was a broadcast sports dataset collected by [1]. However the dataset was a mixture of many different sports captured at highly variable angles and the taks was limited to a categorization exercise.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Diving: A Dataset for Judging Action Quality

Wnuk

Soatto

2011

Computer Vision – ACCV 2010 Workshops

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Abstract. This work presents a unique new dataset and objectives for action analysis. The data presents 3 key challenges: tracking, classification, and judging action quality. The last of these, to our knowledge, has not yet been attempted in the vision literature as applied to sports where technique is scored. This work performs an initial analysis of the dataset with classification experiments, confirming that temporal information is more useful than holistic bag-of-features style analysis in distinguishing dives. Our investigation lays a groundwork of effective tools for working with this type of sports data for future investigations into judging the quality of actions.

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Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition

Cited by 998 publications

References 19 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

Unstructured human activity detection from RGBD images

Analyzing Diving: A Dataset for Judging Action Quality

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