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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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🏛️國立陽明交通大學機構典藏

Author: search.filters.author.Chou, Chien-LiHas files: No

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Now showing 1 - 10 of 16
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    Trajectory Based Jump Pattern Recognition in Broadcast Volleyball Videos
    (2014-01-01)
    Hsu, Chun-Chieh
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    Chen, Hua-Tsung
    ;
    Chou, Chien-Li
    ;
    Ho, Chien-Peng
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    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.
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    Semantic Video Retrieval by Integrating Concept- and Content-Aware Mining
    (2011-01-01)
    Wang, Bo-Wen
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    Su, Ja-Hwung
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    Chou, Chien-Li
    ;
    Tseng, Vincent S.
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    資訊工程學系
    Video retrieval has been a hot topic due to the prevalence of video capturing devices and media-sharing services such as YouTube. Until now, few past studies has focused on querying the videos by images due to the semantic gap between images and videos is not easy to narrow. To this end, in this paper, we propose a novel semantic video retrieval system that integrates web image annotation and concept matching function to bridge images, concepts and videos. For web image annotation, we exploit textual and visual information in the web image to achieve effective image annotation. For concept matching function, we identify the concept relations by calculating the similarity between two concepts via WordNet. On the basis of web image annotation and concept matching function, the proposed system reaches the goals of usability and intelligence on semantic video retrieval. The experimental results reveal that our proposed system can successfully capture the user's intention between image concepts and video concepts for semantic video retrieval.
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    Improving Golf Swing Skills Using Intelligent Glasses
    (2015-01-01)
    Chen, Hua-Tsung
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    Huang, Tzu-Wei
    ;
    Chou, Chien-Li
    ;
    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.
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    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.
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    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.
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    SPEED-ADAPTIVE STREET VIEW IMAGE GENERATION USING DRIVING VIDEO RECORDER
    (2016)
    Chen, Hua-Tsung
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    Eddy, Devi
    ;
    Chen, Ruei-Lin
    ;
    Chou, Chien-Li
    ;
    交大名義發表
    With the rapid development and reduced cost of digital video capturing devices, driving video recorders (DVRs) begin to gain widespread popularity. What is seen and what happens along the way can thus be recorded in videos. However, searching for a specific scene or event among such massive video collections is laborious and tedious. In this paper, we develop a speed-adaptive street view image generation system using general front-mounted DVRs, requiring no additional devices deployed. Visual summaries of street scenes along the way can be provided, allowing users to retrieve a video clip corresponding to a specific road section quickly. An efficient algorithm for estimating the distance a pixel has moved between two consecutive frames is also proposed, so a street view image can be generated with an appropriate aspect ratio without demanding a constant driving speed. Experiments on an extensive data set show that our proposed system can efficiently generate street view images under different lighting and weather conditions, demonstrating its feasibility.
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    Incrementally Mining Temporal Patterns in Interval-based Databases
    (2014)
    Chen, Yi-Cheng
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    Weng, Julia Tzu-Ya
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    Wang, Jun-Zhe
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    Chou, Chien-Li
    ;
    Huang, Jiun-Long
    In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc_TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc_TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets.
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    COHERENT EVENT-BASED SURVEILLANCE VIDEO SYNOPSIS USING TRAJECTORY CLUSTERING
    (2015)
    Chou, Chien-Li
    ;
    Lin, Chin-Hsien
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    Chiang, Tzu-Hsuan
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    Chen, Hua-Tsung
    ;
    Lee, Suh-Yin
    With the rapid development of the camera industry, surveillance systems become more and more popular in our daily life. However, it is very time-consuming to find out specific persons or objects from a mass of surveillance videos with long duration. For efficient browsing surveillance videos, numerous researchers are devoted to eliminating the inherent spatiotemporal redundancy for video synopsis. Nevertheless, too much information in a synopsis frame may distract viewers\' attention. Therefore, we propose a novel surveillance video synopsis system using coherent event classification to alleviate the above issues. Object trajectories are extracted by background subtraction, and then clustered. Coherent events containing similar actions of objects with different moving speeds are obtained by applying the longest common subsequence algorithm to measure the similarity among trajectories. The trajectories in each cluster are rescheduled and stitched onto the background to generate synopsis videos with coherent events. Comprehensive experiments conducted on various surveillance videos demonstrate the convincing performance of our proposed system.
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    MULTI-CAMERA VEHICLE IDENTIFICATION IN TUNNEL SURVEILLANCE SYSTEM
    (2015)
    Chen, Hua-Tsung
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    Chu, Ming-Chu
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    Chou, Chien-Li
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    Lee, Suh-Yin
    ;
    Lin, Bao-Shuh
    Tunnel traffic security has received increasing attention since accidents in tunnels may cause serious casualties. Surveillance cameras are widely equipped in tunnels for traffic condition monitoring and safety maintenance. Vehicle identification among multiple cameras is an essential component in tunnel surveillance systems. In this paper, we propose a Spatiotemporal Successive Dynamic Programming ((SDP)-D-2) algorithm for identifying vehicles between pairs of cameras. Taking color information into consideration, we extract features based on Harris corner detection with OpponentSIFT descriptors. "Tracking-by-identification" for vehicles across multiple cameras can thus be achieved. Extensive experiments on real tunnel video data show that the proposed (SDP)-D-2 algorithm outperforms state-of-the-art methods.
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    A NOVEL VIDEO ANNOTATION FRAMEWORK USING NEAR-DUPLICATE SEGMENT DETECTION
    (2015)
    Chou, Chien-Li
    ;
    Chen, Hua-Tsung
    ;
    Hsu, Chun-Chieh
    ;
    Lee, Suh-Yin
    ;
    資訊工程學系
    The traditional video annotation approaches focus on annotating keyframes, shots, or the whole video with semantic keywords. However, the extractions of keyframes and shots lack of semantic meanings, and it is hard to use a few keywords to describe a video by using multiple topics. Therefore, we propose a novel video annotation framework using near-duplicate segment detection not only to preserve but also to purify the semantic meanings of target annotation units. A hierarchical near-duplicate segment detection method is proposed to efficiently localize near-duplicate segments in frame-level. Videos containing near-duplicate segments are clustered and keyword distributions of clusters are analyzed. Finally, the keywords ranked according to keyword distribution scores are annotated onto the obtained annotation units. Comprehensive experiments demonstrate the effectiveness of the proposed video annotation framework and near-duplicate segment detection method.