Recurrent Bayesian Network for the Recognition of Human Behaviors from Video

Moenne-Loccoz, Nicolas; Brémond, François; Thonnat, Monique

doi:10.1007/3-540-36592-3_7

Cited by 24 publications

(15 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, the use of standard HMMs can be applicable to several types of actions, but other more complex activities with phase variability (e.g. performing an activity one of many ways or in different order) and multi-agent interactions may be more appropriately modeled with coupled [8], hierarchical [16], or switching [38] HMMs or Bayesian networks [27]. However, if the activities can be probabilistically modeled, then the reliable-inference approach can be employed.…”

Section: Discussion Of Resultsmentioning

confidence: 99%

Minimal-latency human action recognition using reliable-inference

Davis

Tyagi

2006

Image and Vision Computing

View full text Add to dashboard Cite

We present a probabilistic reliable-inference framework to address the issue of rapid detection of human actions with low error rates. The approach determines the shortest video exposures needed for low-latency recognition by sequentially evaluating a series of posterior ratios for different action classes. If a subsequence is deemed unreliable or confusing, additional video frames are incorporated until a reliable classification to a particular action can be made. Results are presented for multiple action classes and subsequence durations, and are compared to alternative probabilistic approaches. The framework provides a means to accurately classify human actions using the least amount of temporal information. q

show abstract

Section: Discussion Of Resultsmentioning

confidence: 99%

Minimal-latency human action recognition using reliable-inference

Davis

Tyagi

2006

Image and Vision Computing

View full text Add to dashboard Cite

show abstract

“…Besides this, some researchers perform detection of various kinds of violent behaviors such as fighting, punching, stalking, etc. [16,20,113,136,137].…”

Section: Surveillance Environmentsmentioning

confidence: 99%

A Review on Video-Based Human Activity Recognition

et al. 2013

View full text Add to dashboard Cite

This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems. OPEN ACCESSComputers 2013, 2 89

show abstract

“…Moreover, with the objective of helping the system to control the access safely and comfortably while preventing from fraud, the second next step should consist in human behavior and scenario recognition in order to understand and anticipate the evolutions of mobile objects. For this, we can adapt methods proposed in [3] and [5]. Finally, for the system to be more robust, the third next step will consist in studying the system autonomy.…”

Section: Discussionmentioning

confidence: 99%

“…These systems are usually composed of algorithms for (a) detecting and tracking mobile objects and (b) recognizing mobile object behaviors and related scenarios. In [3], N. Moenne-Locoz and al. use a Recurrent Bayesian Network to model the temporal evolution of the visual features characterizing human behaviors and to infer the occurrences whatever the time-scale.…”

Section: Related Workmentioning

confidence: 99%

Shape recognition based on a video and multi-sensor system

Ngoc

Brémond²,

Thonnat³

et al.

Proceedings. IEEE Conference on Advanced Video and Signal Based Surveillance, 2005.

View full text Add to dashboard Cite

We present in this paper a real-time system for shape recognition. The proposed system is a video and multisensor platform that is able to classify the mobile objects evolving in the scene into several expected categories. The key of the recognition method is to compute mobile object properties thanks to the camera and sensors and then to use Bayesian classifiers. A learning phase based on ground truth data is used to train the Bayesian classifiers.Our recognition method has been integrated into an existing access control device used in public transportation (subway) at RATP to improve safety and comfort, to prevent fraud and to count people for statistical matters. The expected categories in this case are mainly "adult", "child", "suitcase" and "two adults close to each other".

show abstract

Recurrent Bayesian Network for the Recognition of Human Behaviors from Video

Cited by 24 publications

References 6 publications

Minimal-latency human action recognition using reliable-inference

Minimal-latency human action recognition using reliable-inference

A Review on Video-Based Human Activity Recognition

Shape recognition based on a video and multi-sensor system

Contact Info

Product

Resources

About