Applying Space State Models in Human Action Recognition: A Comparative Study

Mendoza, M. Ángeles; Blanca, Nicolás Pérez de la

doi:10.1007/978-3-540-70517-8_6

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…Sminchisescu et al show that CRFs outperform both MEMMs and HMMs when using larger windows, which take into account more of the observation history. These results are partly supported by Mendoza and Pérez de la Blanca [86], who obtain better results for CRFs compared to HMMs using shape features, especially for related actions (e.g. walking and jogging).…”

Section: Discriminative Modelssupporting

confidence: 60%

A survey on vision-based human action recognition

Poppe

2010

Image and Vision Computing

2,025

979

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Section: Discriminative Modelssupporting

confidence: 60%

A survey on vision-based human action recognition

Poppe

2010

Image and Vision Computing

2,025

979

View full text Add to dashboard Cite

“…The technique for classification can either be generative, e.g., HMM [1,19,82], or discriminative, e.g., CRF [44,63]. It can also be as simple as a Nearest Neighbor classifier or more complex methods such as mapping to a Grassmann manifold [23,51].…”

Section: Action Classificationmentioning

confidence: 99%

Action Recognition in Realistic Sports Videos

Soomro

Zamir

2014

Advances in Computer Vision and Pattern Recognition

277

183

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The ability to analyze the actions which occur in a video is essential for automatic understanding of sports. Action localization and recognition in videos are two main research topics in this context. In this chapter, we provide a detailed study of the prominent methods devised for these two tasks which yield superior results for sports videos. We adopt UCF Sports, which is a dataset of realistic sports videos collected from broadcast television channels, as our evaluation benchmark. First, we present an overview of UCF Sports along with comprehensive statistics of the techniques tested on this dataset as well as the evolution of their performance over time. To provide further details about the existing action recognition methods in this area, we decompose the action recognition framework into three main steps of feature extraction, dictionary learning to represent a video, and classification; we overview several successful techniques for each of these steps. We also overview the problem of spatio-temporal localization of actions and argue that, in general, it manifests a more challenging problem compared to action recognition. We study several recent methods for action localization which have shown promising results on sports videos. Finally, we discuss a number of forward-thinking insights drawn from overviewing the action recognition and localization methods. In particular, we argue that performing the recognition on temporally untrimmed videos and attempting to describe an action, instead of conducting a forced-choice classification, are essential for analyzing the human actions in a realistic environment.

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“…Since CRFs directly model the conditional distribution over hidden states given the observations, the conditional independence assumption between observations given the class labels to ensure tractability in HMMs can be relaxed. This difference allows observations at different time instances to be jointly considered, allowing CRFs to handle large contextual dependencies among observations, multiple overlapping observations, and long-range interactions between observations [110,160]. Considering the context and long-term dependencies helps remove ambiguities between similar actions (e.g.…”

Section: Temporal State-space Classifiersmentioning

confidence: 99%

Review of Action Recognition and Detection Methods

Kang,

Wildes

2016

Preprint

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In computer vision, action recognition refers to the act of classifying an action that is present in a given video and action detection involves locating actions of interest in space and/or time. Videos, which contain photometric information (e.g. RGB, intensity values) in a lattice structure, contain information that can assist in identifying the action that has been imaged. The process of action recognition and detection often begins with extracting useful features and encoding them to ensure that the features are specific to serve the task of action recognition and detection. Encoded features are then processed through a classifier to identify the action class and their spatial and/or temporal locations. In this report, a thorough review of various action recognition and detection algorithms in computer vision is provided by analyzing the two-step process of a typical action recognition and detection algorithm: (i) extraction and encoding of features, and (ii) classifying features into action classes. In efforts to ensure that computer vision-based algorithms reach the capabilities that humans have of identifying actions irrespective of various nuisance variables that may be present within the field of view, the state-of-the-art methods are reviewed and some remaining problems are addressed in the final chapter. Dishes sandwich, salad, fried potatoes, potato pancake, omelet, soup, pizza, casserole, mashed potato, snack plate, cake, fruit salad, cold drink, and hot drink Actions background activity, change temperature, cut apart, cut dice, cut in, cut off ends, cut out inside, cut slices, cut stripes, dry, fill water from tap, grate, put on lid, remove lid, mix, move from X to Y, open egg, open tin, open/close cupboard, open/close drawer, open/close fridge, open/close oven, package X, peel, plug in/out, pour, pull out, puree, put in bowl, put in pan/pot, put on bread/dough, put on cutting-board, put on plate, read, remove from package, rip open, scratch off, screw close, screw open, shake, smell, spice, spread, squeeze, stamp, stir, strew, take and put in cupboard, take and put in drawer, take and put in fridge, take and put in oven, take and put in spice holder, take ingredient apart, take out from cupboard, take out from drawer, take out from fridge, take out from oven, take out from spice holder, taste, throw in garbage, unroll dough, wash hands, wash objects, whisk, and wipe clean Category Actions Human-Object Interaction Apply eye makeup, apply lipstick, blow dry hair, brushing teeth, cutting in kitchen, hammering, hula hoop, juggling balls, jump rope, knitting, mixing batter, mopping floor, nun chucks, pizza tossing, shaving beard, skate boarding, soccer juggling, typing, writing on board, and yo-yo Body-Motion Only baby crawling, blowing candles, body weight squats, handstand push-ups, handstand walking, jumping jack, lunges, pull ups, push ups, rock climbing indoor, rope climbing, swing, tight, trampoline jumping, walking with a dog, and wall push-ups Human-Human Interaction band marching, haircut, head...

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Applying Space State Models in Human Action Recognition: A Comparative Study

Cited by 13 publications

References 19 publications

A survey on vision-based human action recognition

A survey on vision-based human action recognition

Action Recognition in Realistic Sports Videos

Review of Action Recognition and Detection Methods

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