A face tracking framework based on convolutional neural networks and Kalman filter

Ren, Zihan; Yang, Shuangyuan; Zou, Fang; Yang, Fan; Luan, Chaoyang; Li, Kai

doi:10.1109/icsess.2017.8342943

Cited by 12 publications

(10 citation statements)

References 7 publications

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“…Compared to Ref. [16] regarding real-time detection and tracking of the human face, there is a difference in this research.This research detects humans by head-shoulder using depth images in RGB-D datasets. Meanwhile, Ref.…”

Section: Cascade Classifiermentioning

confidence: 89%

Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

Angelico

Wardani

2018

CommIT (Communication and Information Technology) Journal

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The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

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Section: Cascade Classifiermentioning

confidence: 89%

Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

Angelico

Wardani

2018

CommIT (Communication and Information Technology) Journal

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“…Nonetheless, our method was able to achieve a better result on the mouth and eyes prediction in comparison to the same predictor using the unprocessed source dataset. The proposed method can be used in many face tracking algorithms that rely on a labeled image training set, such as the work of Ren et al [2017], and the work of Yu and Luo [Yu et al 2016] which are based on CNNs. The method can be applied to other uses such as robotics [Courbon et al 2007] and autonomous vehicles [Bertozzi et al 2015] where specialised camera intrinsics are also often employed.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Recycling a Landmark Dataset for Real-time Facial Capture and Animation with Low Cost HMD Integrated Cameras

Brito

Mitchell

2019

Proceedings of the 17th International Conference on Virtual-Reality Continuum and Its Applications in Industry

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Preparing datasets for use in the training of real-time face tracking algorithms for HMDs is costly. Manually annotated facial landmarks are accessible for regular photography datasets, but introspectively mounted cameras for VR face tracking have incompatible requirements with these existing datasets. Such requirements include operating ergonomically at close range with wide angle lenses, low-latency short exposures, and near infrared sensors. In order to train a suitable face solver without the costs of producing new training data, we automatically repurpose an existing landmark dataset to these specialist HMD camera intrinsics with a radial warp reprojection. Our method separates training into local regions of the source photos, i.e., mouth and eyes for more accurate local correspondence to the mounted camera locations underneath and inside the fully functioning HMD. We combine per-camera solved landmarks to yield a live animated avatar driven from the user's face expressions. Critical robustness is achieved with measures for mouth region segmentation, blink detection and pupil tracking. We quantify results against the unprocessed training dataset and provide empirical comparisons with commercial face trackers. CCS CONCEPTS • Computing methodologies → Motion capture; VR.

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“…Tyan and Kim [32] proposed a compact convolutional neural network (CNN) based visual tracker in conjunction with a particle filter architecture. A face tracking framework based on convolutional neural networks and Kalman filter was proposed for the real-time detecting and tracking of the human face [33,34]. Luo et al [35] proposed a matching Siamese network and CNN-based method to track pedestrian.…”

Section: Related Workmentioning

confidence: 99%

“…upper w = w initial * rate big upper h = h initial * rate big (32) lower w = w initial * rate small lower h = h initial * rate small (33) Upon zoom-in/out, we updated the size of the target model by an aspect of ratio w/h , which Equation 34defines.…”

Section: Byte2 Byte3 Byte7mentioning

confidence: 99%

See 1 more Smart Citation

An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

Chang

Pare

Meena

et al. 2020

BDCC

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At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.

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A face tracking framework based on convolutional neural networks and Kalman filter

Cited by 12 publications

References 7 publications

Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

Recycling a Landmark Dataset for Real-time Facial Capture and Animation with Low Cost HMD Integrated Cameras

An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

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