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Full-Text Articles in Physical Sciences and Mathematics
Human Detection With Contour-Based Local Motion Binary Patterns, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
Human Detection With Contour-Based Local Motion Binary Patterns, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
Associate Professor Wanqing Li
This paper presents a human detection method using contour- based local motion features. The local motion is encoded using a variant of the popular Local Binary Pattern (LBP) called Non-Redundant Local Binary Pattern (NRLBP) descriptor computed on the difference image of two consecutive frames. In addition, the local motion features are extracted along the human's boundary contour. Localising features on the contours has the advantage of utilizing a precise human shape description. A motivation of the proposed method is that most of informative movements are performed on boundary contours of the body parts, e.g. legs of pedestrians. Evaluation of the …
Smoke Detection In Videos Using Non-Redundant Local Binary Pattern-Based Features, Hongda Tian, Wanqing Li, Philip Ogunbona, Duc Thanh Nguyen, Ce Zhan
Smoke Detection In Videos Using Non-Redundant Local Binary Pattern-Based Features, Hongda Tian, Wanqing Li, Philip Ogunbona, Duc Thanh Nguyen, Ce Zhan
Associate Professor Wanqing Li
This paper presents a novel and low complexity method for real-time video-based smoke detection. As a local texture operator, Non-Redundant Local Binary Pattern (NRLBP) is more discriminative and robust to illumination changes in comparison with original Local Binary Pattern (LBP), thus is employed to encode the appearance information of smoke. Non-Redundant Local Motion Binary Pattern (NRLMBP), which is computed on the difference image of consecutive frames, is introduced to capture the motion information of smoke. Experimental results show that NRLBP outperforms the original LBP in the smoke detection task. Furthermore, the combination of NRLBP and NRLMBP, which can be considered …
Illumination Invariant Face Detection Using Classifier Fusion, Alister Cordiner, Philip Ogunbona, Wanqing Li
Illumination Invariant Face Detection Using Classifier Fusion, Alister Cordiner, Philip Ogunbona, Wanqing Li
Associate Professor Wanqing Li
An approach to the problem of illumination variations in face detection that uses classifier fusion is presented. Multiple face detectors are seperately trained for different illumination environments and their results are combined using a combination rule. To define the illumination environments, the training samples are clustered based on their illumination using unsupervised training. Different methods of clustering the samples and combining the outputs of the classifiers are examined. Experiments with the AR face database show that the proposed method achieves higher accuracy than the traditional monolithic face detection method.
A Novel Video-Based Smoke Detection Method Using Image Separation, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona
A Novel Video-Based Smoke Detection Method Using Image Separation, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona
Associate Professor Wanqing Li
In the state-of-the-art video-based smoke detection methods, the representation of smoke mainly depends on the visual information in the current image frame. In the case of light smoke, the original background can be still seen and may deteriorate the characterization of smoke. The core idea of this paper is to demonstrate the superiority of using smoke component for smoke detection. In order to obtain smoke component, a blended image model is constructed, which basically is a linear combination of background and smoke components. Smoke opacity which represents a weighting of the smoke component is also defined. Based on this model, …
An Improved Template Matching Method For Object Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona
An Improved Template Matching Method For Object Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona
Associate Professor Wanqing Li
This paper presents an improved template matching method that combines both spatial and orientation information in a simple and effective way. The spatial information is obtained through a generalized distance transform (GDT) that weights the distance transform more on the strong edge pixels and the orientation information is represented as an orientation map (OM) which is calculated from local gradient. We applied the proposed method to detect humans, cars, and maple leaves from images. The experimental results have shown that the proposed method outperforms the existing template matching methods and is robust against cluttered background.