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Full-Text Articles in Physical Sciences and Mathematics
The Detection Of Steam Injection Based On Video Surveillance, Yipeng Guo, Shu Wang, Ming Zhu, Jiangtao Xi
The Detection Of Steam Injection Based On Video Surveillance, Yipeng Guo, Shu Wang, Ming Zhu, Jiangtao Xi
Faculty of Informatics - Papers (Archive)
In this paper, we focus on the visual features of steam injection and propose an integrated algorithm to detect it based on video surveillance. The proposed method is depended on three decision rules which are the attribute of gray level and the feature of frequent flicker rate of steam injection, and the similarity structure between background image and current frame. The block-based approach is applied to all three decision rules. The experimental results show that the algorithm provides a reliable detection method which is useful in many cases such as the alarm on the leakage of a heating pipe.
Object Detection Using Non-Redundant Local Binary Patterns, Duc Thanh Nguyen, Zhimin Zong, Philip Ogunbona, Wanqing Li
Object Detection Using Non-Redundant Local Binary Patterns, Duc Thanh Nguyen, Zhimin Zong, Philip Ogunbona, Wanqing Li
Faculty of Informatics - Papers (Archive)
Local Binary Pattern (LBP) as a descriptor, has been successfully used in various object recognition tasks because of its discriminative property and computational simplicity. In this paper a variant of the LBP referred to as Non-Redundant Local Binary Pattern (NRLBP) is introduced and its application for object detection is demonstrated. Compared with the original LBP descriptor, the NRLBP has advantage of providing a more compact description of object’s appearance. Furthermore, the NRLBP is more discriminative since it reflects the relative contrast between the background and foreground. The proposed descriptor is employed to encode human’s appearance in a human detection task. …
Human Detection Using Local Shape And Non-Redundant Binary Patterns, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona
Human Detection Using Local Shape And Non-Redundant Binary Patterns, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona
Faculty of Informatics - Papers (Archive)
Motivated by the advantages of using shape matching technique in detecting objects in various postures and viewpoints and the discriminative power of local patterns in object recognition, this paper proposes a human detection method combining both shape and appearance cues. In particular, local shapes of the body parts are detected using template matching. Based on body parts' shapes, local appearance features are extracted. We introduce a novel local binary pattern (LBP) descriptor, called Non-Redundant LBP (NRLBP), to encode local appearance of human. The proposed method was evaluated and compared with other state-of-the-art human detection methods on two commonly used datasets: …
Markov Random Fields For Abnormal Behavior Detection On Highways, Son Lam Phung, Philippe L. Bouttefroy, Abdesselam Bouzerdoum, Azeddine Beghdadi
Markov Random Fields For Abnormal Behavior Detection On Highways, Son Lam Phung, Philippe L. Bouttefroy, Abdesselam Bouzerdoum, Azeddine Beghdadi
Faculty of Informatics - Papers (Archive)
This paper introduces a new paradigm for abnormal behavior detection relying on the integration of contextual information in Markov random fields. Contrary to traditional methods, the proposed technique models the local density of object feature vector, therefore leading to simple and elegant criterion for behavior classification. We develop a Gaussian Markov random field mixture catering for multi-modal density and integrating the neighborhood behavior into a local estimate. The convergence of the random field is ensured by online learning through a stochastic clustering algorithm. The system is tested on an extensive dataset (over 2800 vehicles) for behavior modeling. The experimental results …