Integrated detection and tracking of multiple faces using particle filtering and optical flow-based elastic matching

Bhandarkar, Suchendra M.; Luo, Xingzhi

doi:10.1016/j.cviu.2008.11.010

Cited by 13 publications

(6 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khan et al [34] proposed a probabilistic model to affiliate merged and split components using a MCMC-based particle filter. Yu and Medioni [20] extended the work of Songhwaiet al [16] to detect the appropriate temporal and spatial affiliation of segments with a Data-Driven MCMC sampling approach.…”

Section: Multi-track Linkingmentioning

confidence: 99%

A literature review on recent multi-object tracking methods based on HMM and particle filter

Ahire¹,

Mohod²

2014

IOSRJCE

View full text Add to dashboard Cite

Tracking of the object movement in the input frame of video is an important process for various real time application such as video-conferencing, human robotics or human computer interface or in the analysis of social interaction. The important step is to determine the path of the object. The various techniques to track the multiple moving objects in a input frame of video have been proposed. This paper gives a brief analysis of recent multi-object trackers algorithms based on markov model and particle filtering Keywords: face tracking,HMM(Hidden markov model)MCMC(Markov Chain Monte Carlo), PF(Particle filtering), long term multi-object tracking.

show abstract

Section: Multi-track Linkingmentioning

confidence: 99%

A literature review on recent multi-object tracking methods based on HMM and particle filter

Ahire¹,

Mohod²

2014

IOSRJCE

View full text Add to dashboard Cite

show abstract

“…Optical flow-based face representation has attracted much attention [19]. According to the physiology, the expression is a dynamic event; it must be represented by the motion information of a face.…”

Section: Facial Expression Representationmentioning

confidence: 99%

Intelligent Detection of Facial Expression based on Image

Zhu

Xiao

2015

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

Abstract-Human facial expressions detection plays a central role in pervasive health care and it is an active research field in computer vision. In this paper, a novel method for facial expression detection from dynamic facial images is proposed, which includes two stages of feature extraction and facial expression detection. Firstly, Active Shape Model (ASM) is used to extract the local texture feature, and optical flow technique is determined facial velocity information, which is used to characterize facial expression. Then, fusing the local texture feature and facial velocity information get the hybrid characteristics using Bag of Words. Finally, Multi-Instance Boosting model is used to recognize facial expression from video sequences. In order to be learned quickly and complete the detection, the class label information is used for the learning of the Multi-Instance Boosting model. Experiments were performed on a facial expression dataset built by ourselves and on the JAFFE database to evaluate the proposed method. The proposed method shows substantially higher accuracy at facial expression detection than has been previously achieved and gets a detection accuracy of 95.3%, which validates its effectiveness and meets the requirements of stable, reliable, high precision and anti-interference ability etc.

show abstract

“…[2], [3], [4], [5], [6]), mainly focusing on new features, new multi-cue fusion mechanisms, better dynamics or adaptive models for instance [7], [8], [9], [10], and results are demonstrated mostly on short sequences [7], [8], [9], [10].…”

Section: A Motivationmentioning

confidence: 99%

Track Creation and Deletion Framework for Long-Term Online Multiface Tracking

Duffner

Odobez

2013

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-To improve visual tracking, a large number of papers study more powerful features, or better cue fusion mechanisms, adaptation or contextual models, for instance. A complementary approach consists in improving the track management, that is, deciding when to add a target or stop its tracking, for example in case of failure. This is an essential component for effective multi-object tracking applications, and is often not trivial. Deciding to stop a track or not is a compromise between avoiding erroneous early stopping while tracking is fine, and erroneous continuation of tracking when there is an actual failure. This decision process, very rarely addressed in the literature, is difficult due to, for example, object detector deficiencies or observation models that are insufficient to describe the full variability of tracked objects and deliver reliable likelihood (tracking) information. This paper addresses the track management issue and presents a real-time, online multi-face tracking algorithm that effectively deals with the above difficulties. The tracking itself is formulated in a multiobject state-space Bayesian filtering framework solved with Markov Chain Monte Carlo. Within this framework, an explicit probabilistic filtering step decides when to add or remove a target from the tracker, where decisions rely on multiple cues such as face detections, likelihood measures, long term observations, and track state characteristics. The method has been applied to three challenging datasets of more than 9 hours in total, and demonstrate a significant performance increase compared to more traditional approaches (MCMC, RJ-MCMC) only relying on head detections and likelihoods for track management.

show abstract

Integrated detection and tracking of multiple faces using particle filtering and optical flow-based elastic matching

Cited by 13 publications

References 26 publications

A literature review on recent multi-object tracking methods based on HMM and particle filter

A literature review on recent multi-object tracking methods based on HMM and particle filter

Intelligent Detection of Facial Expression based on Image

Track Creation and Deletion Framework for Long-Term Online Multiface Tracking

Contact Info

Product

Resources

About