Browsing by Author "Jen, Cheng-Lung"

Now showing 1 - 7 of 7

A Relative-Discriminative-Histogram-of-Oriented-Gradients-Based Particle Filter Approach to Vehicle Occlusion Handling and Tracking
(2014-08-01)
Wu, Bing-Fei
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Kao, Chih-Chung
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Jen, Cheng-Lung
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Li, Yen-Feng
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Chen, Ying-Han
This paper presents a relative discriminative histogram of oriented gradients (HOG) (RDHOG)-based particle filter (RDHOGPF) approach to traffic surveillance with occlusion handling. Based on the conventional HOG, an extension known as RDHOG is proposed, which enhances the descriptive ability of the central block and the surrounding blocks. RDHOGPF can be used to predict and update the positions of vehicles in continuous video sequences. RDHOG was integrated with the particle filter framework in order to improve the tracking robustness and accuracy. To resolve multiobject tracking problems, a partial occlusion handling approach is addressed, based on the reduction of the particle weights within the occluded region. Using the proposed procedure, the predicted trajectory is closer to that of the real rigid body. The proposed RDHOGPF can determine the target by using the feature descriptor correctly, and it overcomes the drift problem by updating in low-contrast and very bright situations. An empirical evaluation is performed inside a tunnel and on a real road. The test videos include low viewing angles in the tunnel, low-contrast and bright situations, and partial and full occlusions. The experimental results demonstrate that the detection ratio and precision of RDHOGPF both exceed 90%.
Accompanist Recognition and Tracking for Intelligent Wheelchairs
(2014)
Wu, Bing-Fei
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Jen, Cheng-Lung
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Tsou, Tai-Yu
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Chen, Po-Yen
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電控工程研究所
Recently, several robotic wheelchairs have been proposed that employ autonomous functions. In designing wheelchairs, it is important to reduce the accompanist load. To provide such a task, the mobile robot needs to recognize and track people. In this paper, we propose to utilize the multisensory data fusion to track a target accompanist. First, the simultaneous localization and map building is achieved by using the laser range finder (LRF) and inertial sensors with the extended Kalman filter recursively. To track the target person robustly, the accompanist, are tracked by fusing laser and vision data. The human objects are detected by LRF, and the identity of accompanist is recognized using a PTZ camera with a pre-defined signature using the speed-up robust features algorithm. The proposed system can adaptively search visual signature and track the accompanist by dynamically zooming the PTZ camera based on LRF detection results to enlarge the range of human following. The experimental results verified and demonstrated the performance of the proposed system.
ACTIVE APPEARANCE MODEL ALGORITHM WITH K-NEAREST NEIGHBOR CLASSIFIER FOR FACE POSE ESTIMATION
(2014-06-01)
Wu, Bing-Fei
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Kao, Chih-Chung
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Jen, Cheng-Lung
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Chiang, Chia-Rong
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Lai, Po-Hung
In this paper, a face pose estimation (FPE) algorithm using active appearance model (AAM) with a k-nearest neighbor (kNN) classifier is presented. AAM is a model-based image representation method used to describe non-rigid visual objects, with both shape and texture variations, using a mean vector and linear combinations of a set of variation modes. Since AAM is a deformable model, it has several variations. Owing to the variations, the model is adjusted to the input test face image using iterative searching and fitting. The error, which measures the difference between the model and a test image, is minimized with the proposed searching algorithm. The face pose is then estimated using the distances between the landmark points in the AAM model with a INN classifier. Experimental results demonstrate that the proposed FPE algorithm can fit the face location with different face poses, with or without a hat, even wearing glasses, and identify the face pose accurately.
Adaptive Online Learning for Human Tracking
(2013-01-01)
Wu, Bing-Fei
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Tseng, Pin-Yi
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Jen, Cheng-Lung
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Tsou, Tai-Yu
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Hsiao, Kai-Tse
In this work, we present a multiple classifiers system cascades an on-line learning RGB-D appearance model framework in which detection, recognition, and tracking are highly coupled for a wheelchair robot equipped with a Kinect sensor to improve the efficiency of the care assistance and quality of accompanying service. The on-line trained classifiers use the surrounding background as negative examples in the updating which allows the algorithm to choose the most discriminative features between the target and the background, incrementally adjust to the changes in specific tracking environment. Meanwhile, a depth clustering based human detection is proposed to extract human candidates. Accordantly, an on-line learning RGB-D appearance model is cascaded to strengthen the human tracking function by dealing with color, depth and position information from the identified caregiver. Consequently, several experiments have been conducted to demonstrate the effectiveness and feasibility in real world environments.
Neural fuzzy based indoor localization by kalman filtering with propagation channel Modeling
(2007)
Wu, Bing-Fei
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Jen, Cheng-Lung
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Chang, Kuei-Chung
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電控工程研究所
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Institute of Electrical and Control Engineering
In this study, an indoor localization based on the received signal strength indication (RSSI) in wireless sensor networks (WSN) is proposed. The presented approach proceeds in two phases: the first phase is based on the recorded received signal strength at the certain location. The interpolation, curve fitting and an adaptive neural fuzzy inference system (ANFIS) are used to develop the indoor propagation model, respectively. Thus the strength of the received radio signal can be converted to a physical distance approximately; in the second phase, based on the available distances from the positions localized in the test bed are estimated by using an extended Kalman filter (EKF). In comparison among the propagation models based on the interpolation, ANFIS and curve fitting, the experimental results show that the proposed approach provides a precise performance.
Particle-Filter-Based Radio Localization for Mobile Robots in the Environments With Low-Density WLAN APs
(2014-12-01)
Wu, Bing-Fei
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Jen, Cheng-Lung
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交大名義發表
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電控工程研究所
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National Chiao Tung University
This paper proposes a new localization method for mobile robots based on received signal strength (RSS) in indoor wireless local area networks (WLANs). In indoor wireless networks, propagation conditions are very difficult to predict due to interference, reflection, and fading effects. As a result, an explicit measurement equation is not available. In this paper, an observation likelihood model is accomplished using kernel density estimation to characterize the dependence of location and RSS. Based on the measured RSS, the robot\'s location is dynamically estimated using the proposed adaptive local search particle filter (ALSPF), which adopts the covariance adaptation for correcting the system states and updating the motion uncertainty. To deal with low sensor density in large-space environments, we present a strategy based on the strongest signal with minimum variance to choose a subset of detectable access points (APs) for enhancing robot localization and reducing the computational burden. The proposed approaches are verified by realistic low-density WLAN APs to demonstrate the feasibility and suitability. Experimental results indicate that the proposed ALSPF provides approximately 1-m error and significant improvements over particle filtering.
RGB-D Sensor Based SLAM and Human Tracking with Bayesian Framework for Wheelchair Robots
(2013)
Wu, Bing-Fei
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Jen, Cheng-Lung
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Li, Wun-Fang
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Tsou, Tai-Yu
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Tseng, Pin-Yi
In this paper, we present an approach to visual SLAM and human tracking for a wheelchair robot equipped with a Microsoft Kinect sensor that which is a novel sensing system that captures RGB and depth (RGB-D) images simultaneously. The speeded-up robust feature (SURF) algorithm is employed to provide the robust description of feature for environments and the target person from RGB images. Based on the environmental SURF features, we present the natural landmark based simultaneous localization and mapping with the extended Kalman filter suing RGB-D data. Meanwhile, a depth clustering based human detection is proposed to extract human candidates. Accordantly, the target person tracking is achieved with an online learned RGB-D appearance model by integrating histogram orientation of gradient descriptor, color, depth, and position information from the body of the identified caregiver. Moreover, a fuzzy based controller provides dynamical human following for the wheelchair robot with a desired interval. Consequently, the experimental results demonstrated the effectiveness and feasibility in real world environments.