學術出版;;Publications

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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Trajectory Based Jump Pattern Recognition in Broadcast Volleyball Videos
(2014-01-01)
Hsu, Chun-Chieh
;
Chen, Hua-Tsung
;
Chou, Chien-Li
;
Ho, Chien-Peng
;
Lee, Suh-Yin
Jump actions are typically accompanied by spiking and imply significant events in volleyball matches. In this paper, we propose an effective system capable of jump pattern recognition in player moving trajectories from long broadcast volleyball videos. First, the entire video is segmented into clips of rallies by shot segmentation and whistle detection. Then, camera calibration is adopted to find the correspondence between coordinates in the video frames and real-world coordinates. With the homographic transformation matrix computed, real-world player moving trajectories can be derived by a sequence of tracked player locations in video frames. Jump patterns are recognized from the player moving trajectory by using a sliding window scheme with physics-based validation and context constraint. Finally, the jump locations can be estimated and jump tracks can be separated from the planar moving tracks. The experiments conducted on broadcast volleyball videos show promising results.
Fast Multi-View People Localization Using a Torso-High Reference Plane
(2017-01-01)
Hsu, Chun-Chieh
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Chen, Hua-Tsung
;
Tsai, Wen-Jiin
;
Lee, Suh-Yin
;
交大名義發表
People locations bring rich information for a wide spectrum of applications in intelligent video surveillance systems. In addition to localization accuracy, computational efficiency is another significant issue to be highly concerned in people localization. As an essential early stage, people localization has to be accomplished in a very short time, enabling further semantic analysis. However, most state-of-the-art people localization methods pay little attention to computational efficiency. Hence, in this paper we propose an effective and efficient multi-view people localization scheme with several acceleration mechanisms. First, a torso-high reference plane is introduced since in general the torso part (after foreground segmentation) is more intact and stable than the other parts of a human body, and thus can predict potential people locations more reliably. Then, a novel and computationally efficient bitwise-operation scheme is proposed to predict people locations at the intersection regions of foreground line samples from multiple views. After rule-based validation, people locations can be accurately obtained and visualized on a real world plane. Experiments on multi-view surveillance videos not only validate the high accuracy of the proposed method in locating people under crowded scenes with serious occlusions, but also demonstrate an outstanding computational speed.
A System for Front Vehicle Detection and Fast Indexing Using a Single Car-Mounted Camera
(2015-01-01)
Hsu, Chun-Chieh
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Wu, Yi-Chien
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Chen, Hua-Tsung
;
Lee, Suh-Yin
;
交大名義發表
Video safety becomes a recently arising issue in the automobile industry due to the rapid expansion of car ownership worldwide. Increasing driver assistance systems have been developed for security and accident prevention by continuously monitoring the vehicle surroundings and the driving behaviors to detect potential problems in advance. Since most traffic/criminal accidents are caused by vehicles, recognizing and tracking the fleeing vehicles on the roads are of vital importance. In this paper, we propose a vision-based front vehicle detection and fast indexing system using a single car-mounted camera, which tends to be widespread deployed. First, front vehicle candidates are detected by a cascade of Gentle Adaboost classifiers utilizing Histogram of Oriented Gradients features. To improve the detection accuracy, symmetry of taillights are further employed to filter out the false detected candidates. Once a vehicle is detected, the GPS locations are reported to a central server, so that a database of detections can be maintained and can be provided to the related departments for timely retrieving and quickly indexing the searched vehicles on the maps. Experiments conducted on real videos demonstrate that satisfactory results can be obtained by the proposed detection scheme.
A Robust Vision-Based Heart Rate Evaluation
(2015-01-01)
Lin, Kuan-Yi
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Chen, Duan-Yu
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Lee, Suh-Yin
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資訊工程學系
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Department of Computer Science
Monitoring heart rates using conventional electrocardiogram (ECG) equipment requires patients to wear adhesive gel patches or chest straps that can cause skin irritation and discomfort. Commercially-available pulse oximetry sensors that attach to the fingertips or earlobes also cause inconvenience for patients and the spring-loaded clips can be painful to use. Therefore, a novel robust face-based heart rate monitoring technique is proposed to allow for the evaluation of heart rate variation without physical contact with the patient. Face reflectance is first decomposed from a single image and then heart rate evaluation is conducted from consecutive frames according to the periodic variation of reflectance strength resulting from changes to hemoglobin absorptivity across the visible light spectrum as heartbeats cause changes to blood volume in the blood vessels in the face. To achieve a robust evaluation, ensemble empirical mode decomposition (EEMD) of the Hilbert-Huang Transform (HHT) is used to acquire the primary heart rate signal while reducing the effect of ambient light changes. Our proposed approach is found to outperform the current stateo-f-the-art, providing greater measurement accuracy with smaller variance and is shown to be feasible in real-world environments.
Computer-assisted Billiard Self-Training Using Intelligent Glasses
(2017-01-01)
Hsu, Chun-Chieh
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Tsai, Hou-Chun
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Chen, Hua-Tsung
;
Tsai, Wen-Jiin
;
Lee, Suh-Yin
Self-training plays an important role in sports exercise. However, if not under the instruction of a coach, it would be ineffective for most amateurs or inexperienced players to exercise on their own. Therefore, establishing computer-assisted training systems for sports exercise is a recently emerging topic. In this paper, we propose a billiard self-training system, which aims at improving billiard players' performance by utilizing intelligent glasses as a wearable camera and displayer. The proposed system is able to automatically analyze user-captured images of the billiard table from multiple views and display the ball configurations on a virtual top-view table. Enriched visual presentation can be provided to give the practitioner a further sight into the game. The experiments conducted on sixteen sets of different ball configurations show promising results.
Traffic Congestion Classification for Nighttime Surveillance Videos
(2012-01-01)
Chen, Hua-Tsung
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Tsai, Li-Wu
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Gu, Hui-Zhen
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Lee, Suh-Yin
;
Lin, Bao-Shuh P.
Traffic surveillance systems have been widely used for traffic monitoring. If the degree of traffic congestion can be evaluated from the surveillance videos immediately, the drivers can choose alternate routes to avoid traffic jam when traffic congestion arises. Compared to daytime surveillance, some tough factors such as poor visibility and higher noise increase the difficulty in video understanding under nighttime environments. In this paper, we propose a framework of traffic congestion classification for nighttime surveillance videos. The framework consists of three steps: the first one is to detect headlights based on three salient headlight features. Second, headlights are grouped into individual vehicles by evaluating their correlations. Third, a virtual detection line is adopted to gather the traffic information for traffic congestion evaluation. Then the traffic congestion is classified into five levels: jam, heavy, medium, mild and low in real-time. We use freeway nighttime surveillance videos to demonstrate the performances on accuracy and computation. Satisfactory experimental results validate the effectiveness of the proposed framework.
Improving Golf Swing Skills Using Intelligent Glasses
(2015-01-01)
Chen, Hua-Tsung
;
Huang, Tzu-Wei
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Chou, Chien-Li
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Tsai, Hou-Chun
;
Lee, Suh-Yin
Since a proficient golf swing is a key element in success in golf, golfers always make much effort in improving their swing skills. However, if not under the supervision of a coach, a golfer cannot effectively perceive his/her faults in swings and comprehend how to change the swing mechanics. In this paper, we propose a vision-based golf training system, named "Improve My Golf Swing," which aims at improving the golfer's swing skills using intelligent glasses serving as a wearable displayer. The golfer's posture is analyzed through computation-efficient image processing techniques. Involving professional golf training knowledge, the proposed system automatically generates feature lines to evaluate whether the golfer's body is properly aligned. During a swing, the golfer can watch his/her own posture as well as the augmented feature lines displayed on the intelligent glasses. Instead of quantitative swing analysis, we focus on providing instructions of posture adjustment to the golfer in a more intuitive and unambiguous manner. Different audio alerts will be given to the golfer according to which part of a posture fault is detected. Experiments on different golf swing videos show encouraging results.
Multimodal Video-to-Near-Scene Annotation
(2017-02)
Chou, Chien-Li
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Chen, Hua-Tsung
;
Lee, Suh-Yin
;
交大名義發表
;
資訊工程學系
Traditional video annotation approaches focus on annotating keyframes/shots or whole videos with semantic keywords. However, the extraction processes of keyframes/shots might lack semantic meanings, and it is hard to use a few keywords to describe the content of a long video with multiple topics. In this work, near- scenes, which contain similar concepts, topics, or semantic meanings, are designed for better video content understanding and annotation. We propose a novel framework of hierarchical video-to-near-scene annotation not only to preserve but also to purify the semantic meanings of near- scenes. To detect near-scenes, a pattern- based prefix tree is first constructed to fast retrieve near- duplicate videos. Then, the videos containing similar near- duplicate segments and similar keywords are clustered with consideration of multimodal features including visual and textual features. To enhance the precision of near-scene detection, a pattern-to-intensity- mark (PIM) method is proposed to perform precise frame- level near- duplicate segment alignment. For each near- scene, a video-to-concept distribution model is designed to analyze the representativeness of keywords and discriminations of clusters by the proposed potential term frequency and inverse document frequency and entropy. Tags are ranked according to video-to-concept distribution scores, and the tags with the highest scores are propagated to near-scenes detected. Extensive experiments demonstrate that the proposed PIM outperforms state-of-the-art approaches compared in terms of quality segments and quality frames for near-scene detection. Furthermore, the proposed framework of hierarchical video- to-near- scene annotation can achieve high quality of near-scene annotation in terms of mean average precision.
2D Histogram-based player localization in broadcast volleyball videos
(2016-06)
Hsu, Chun-Chieh
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Chen, Hua-Tsung
;
Chou, Chien-Li
;
Lee, Suh-Yin
;
資訊工程學系
Player location is one of the most informative cues for obtaining tactics arrangement and collecting descriptive game statistics. However, state-of-the-art supervised learning-based methods for player localization require a large amount of labeled training data. Hence, the development of automatic systems for player localization becomes indispensable. Volleyball games reach a huge audience base and contain a variety of tactical strategies, necessitating the implementation of systems for inferring tactics and analyzing formations automatically. Therefore, a novel 2D histogram-based player localization method capable of locating players with occlusions is developed and presented in this paper. The proposed system is able to automatically detect the court lines for camera calibration, extract players by calculating both x and y histograms of extracted player masks, and visualize the team formations on real-world court model. The experiments on broadcast volleyball videos demonstrate efficient and effective results against a traditional object segmentation method (connected component analysis) and a supervised learning approach utilizing histogram of oriented gradient features.
Resource-aware Replication Strategies in P2P VoD Services with Scalable Video Streams
(2012)
Ho, Chien-Peng
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Sun, Yu-Chen
;
Yu, Jen-Yu
;
Lee, Suh-Yin
;
資訊工程學系
This article aims to discuss the replication principle that underlies correlations between peer-network conditions and video quality impacts over unstructured peer-to-peer (P2P) video-on-demand (VoD) overlay networks. In particular, the conditions of a network path among peers, such as communication latencies, packet buffer status, and upload bandwidth, can be used to develop failover mechanisms that enable recovery from a failure in part of the P2P VoD system. The failure recovery can be accomplished by using the resource-aware replication strategy, which can deploy high video quality impacts and popular media-data replicas to the movie time-scale of low averaged resource rate and discontinuous area. The simulation results demonstrate that replicas of popular and high video-distortion data are significant and reflect physical demands. In addition, the averaged visual quality of video can be enhanced, and the load imposed on a VoD server can be reduced, through facilitating proper progression of replication strategy.

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