Multimodal Approach to Human-Face Detection and Tracking

Vadakkepat, Prahlad; Lim, Pei Li; Silva, Liyanage C. De; Liu, Jing; Ling, Li Li

doi:10.1109/tie.2007.903993

Cited by 160 publications

(75 citation statements)

References 20 publications

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“…The first methods for rigid 2D tracking generally revolved around the use of various features or transformations and mainly explored various color-spaces for robust tracking (Crowley and Berard 1997;Bradski 1998b;Qian et al 1998;Toyama 1998;Jurie 1999;Schwerdt and Crowley 2000;Stern and Efros 2002;Vadakkepat et al 2008). The general methods of choice for tracking were Mean Shift and variations such as the Continuously Adaptive Mean Shift (Camshift) algorithm (Bradski 1998a;Allen et al 2004).…”

Section: Prior Artmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-theart deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

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Section: Prior Artmentioning

confidence: 99%

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

et al. 2017

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“…Several authors have proposed the use of optical flow estimation to aid in the track of faces between frames [33,34,35,36,37,32]. Other works, not discussed here, make use for example of mean-shift [38,39,40,41,42,43], particle filters [44,45,46,47], Kalman filters [48,49,50], Active Contour Models [34,35,51], wavelet transform [52] or Bayesian Networks [53], or Markov Random Fields [54]. This list is not exhaustive but only gives works published after 2000.…”

Section: Face Trackingmentioning

confidence: 99%

Real‐time AdaBoost cascade face tracker based on likelihood map and optical flow

et al. 2017

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The authors present a novel face tracking approach where optical flow information is incorporated into a modified version of the Viola-Jones detection algorithm. In the original algorithm, detection is static, as information from previous frames is not considered; in addition, candidate windows have to pass all stages of the classification cascade, otherwise they are discarded as containing no face. In contrast, the proposed tracker preserves information about the number of classification stages passed by each window. Such information is used to build a likelihood map, which represents the probability of having a face located at that position. Tracking capabilities are provided by extrapolating the position of the likelihood map to the next frame by optical flow computation. The proposed algorithm works in real time on a standard laptop. The system is verified on the Boston Head Tracking Database, showing that the proposed algorithm outperforms the standard Viola-Jones detector in terms of detection rate and stability of the output bounding box, as well as including the capability to deal with occlusions. The authors also evaluate two recently published face detectors based on convolutional networks and deformable part models with their algorithm showing a comparable accuracy at a fraction of the computation time.

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“…The challenges of this problem for different applications and situations are not the same. Many of previous works have been focused on tracking of human face in videos [4,5]. Others have been concerned on robotic applications of object tracking [6,7,8].…”

Section: Related Workmentioning

confidence: 99%

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

Saadian

2017

Teh. vjesn.

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Original scientific paper In this paper a new method based on evolutionary clustering and locally linear neuro-fuzzy (LLNF) models is proposed for the problem of object tracking in videos. This approach utilizes clustering on color feature space to obtain a model of object which is given at the initial frame. To achieve the optimal clustering, evolutionary optimization methods are used. Based on the results of clustering, parameters of LLNF model is determined so it can be used as an identifier of object during the real time video streaming. To track the object, a swarm of weighted evolving linear models are used to estimate the location and size of the object at next frame based on its current and previous states. The performance of the proposed method is evaluated on a benchmark data set and compared to other methods performed on the same data set. The results show that the accuracy of the proposed method is superior to previous methods. Keywords: clustering; evolutionary computation; Locally Linear Neuro-Fuzzy model; object tracking; swarm optimization Praćenje predmeta u video snimkama pomoću modela evolucijskog grupiranja i lokalno linearnih neuro-fuzzy modelaIzvorni znanstveni članak U radu se za problem praćenja objekta u video snimkama predlaže nova metoda koja se zasniva na modelima evolucijskog grupiranja i lokalno linearnim neuro-fuzzy modelima (LLNF). Taj pristup koristi grupiranje na prostoru karakteristike u boji kako bi se dobio model predmeta zadanog u početnom okviru. Za postizanje optimalnog grupiranja, primijenjene su metode evolucijske optimizacije. Na temelju rezultata grupiranja određeni su parametri LLNF modela te se on može rabiti kao identifikator objekta tijekom video prijenosa u realnom vremenu. Za praćenje objekta koristi se roj ponderiranih izvedenih linearnih modela za procjenu lokacije i veličine objekta u sljedećem okviru na temelju njegovog postojećeg i prethodnih stanja. Učinkovitost predložene metode procijenjena je na referentnom nizu podataka i uspoređena s drugim metodama provedenim na istom nizu podataka. Rezultati pokazuju da je po točnosti predložena metoda bolja od prethodnih.

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Multimodal Approach to Human-Face Detection and Tracking

Cited by 160 publications

References 20 publications

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

Real‐time AdaBoost cascade face tracker based on likelihood map and optical flow

Object tracking in videos by evolutionary clustering and locally linear neuro-fuzzy models

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