Browsing by Author "Chou, Chien-Li"

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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
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Chou, Chien-Li
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Lee, Suh-Yin
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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.
A NOVEL VIDEO ANNOTATION FRAMEWORK USING NEAR-DUPLICATE SEGMENT DETECTION
(2015)
Chou, Chien-Li
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Chen, Hua-Tsung
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Hsu, Chun-Chieh
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Lee, Suh-Yin
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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.
Automatic Restaurant Information and Keyword Extraction by Mining Blog Data for Chinese Restaurant Search
(2014-01-01)
Chou, Chien-Li
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Tsai, Min-Ho
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Chao, Chien-Ho
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Lin, Hsiao-Jung
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Chen, Hua-Tsung
Restaurant search and recommendation system is a very popular service in many countries. In those systems, most of the restaurant information such as restaurant name, address, phone number, and introduction are collected manually. In this paper, we propose a restaurant information extraction method which can automatically extract restaurant information from online reviews of restaurants in blogs. In addition, by calculating TFIDFs of words in blog posts, the hot keywords can be discovered and ranked. For restaurant search, users are allowed to search by keywords, areas, and/or extracted hot keywords. The experimental results show that the proposed method can achieve over 90 % average accuracy of hot keyword extraction and about 95 % mean average precision for restaurant search. In user study, the fact that the proposed system is more useful than Google search in restaurant search is presented.
COHERENT EVENT-BASED SURVEILLANCE VIDEO SYNOPSIS USING TRAJECTORY CLUSTERING
(2015)
Chou, Chien-Li
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Lin, Chin-Hsien
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Chiang, Tzu-Hsuan
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Chen, Hua-Tsung
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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.
COMPUTER-ASSISTED SELF-TRAINING SYSTEM FOR SPORTS EXERCISE USING KINECTS
(2013)
Chen, Hua-Tsung
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He, Yu-Zhen
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Chou, Chien-Li
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Lee, Suh-Yin
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Lin, Bao-Shuh P.
Self-training plays an important role in sports exercise. However, if not under the instruction of a coach, improper training postures can cause serious harm to muscles and ligaments of the body. Hence, the development of computer-assisted self-training systems for sports exercise is a recently emerging research topic. In this paper, we propose a Yoga self-training system, entitled YogaST, which aims at instructing the user/practitioner to perform the asana (Yoga posture) correctly and preventing injury caused by improper postures. Involving professional Yoga training knowledge, YogaST analyzes the practitioner's posture from both front and side views using two Kinects with perpendicular viewing directions and assists him/her in rectifying bad postures. The contour, skeleton, and feature axes of the human body are extracted as posture representation. Then, YogaST analyzes the practitioner's posture and presents visualized instruction for posture rectification so that the practitioner can easily understand how to adjust his/her posture.
Effective Semantic Annotation by Image-to-Concept Distribution Model
(2011-06-01)
Su, Ja-Hwung
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Chou, Chien-Li
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Lin, Ching-Yung
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Tseng, Vincent S.
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資訊工程學系
Image annotation based on visual features has been a difficult problem due to the diverse associations that exist between visual features and human concepts. In this paper, we propose a novel approach called Annotation by Image-to-Concept Distribution Model (AICDM) for image annotation by discovering the associations between visual features and human concepts from image-to-concept distribution. Through the proposed image-to-concept distribution model, visual features and concepts can be bridged to achieve high-quality image annotation. In this paper, we propose to use "visual features", "models", and "visual genes" which represent analogous functions to the biological chromosome, DNA, and gene. Based on the proposed models using entropy, tf-idf, rules, and SVM, the goal of high-quality image annotation can be achieved effectively. Our empirical evaluation results reveal that the AICDM method can effectively alleviate the problem of visual-to-concept diversity and achieve better annotation results than many existing state-of-the-art approaches in terms of precision and recall.
EXTRACTION AND REPRESENTATION OF HUMAN BODY FOR PITCHING STYLE RECOGNITION IN BROADCAST BASEBALL VIDEO
(2011)
Chen, Hua-Tsung
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Chou, Chien-Li
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Tsai, Wen-Jiin
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Lee, Suh-Yin
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Yu, Jen-Yu
In baseball games, different release points of pitchers form several kinds of pitching styles. Different pitching styles possess individual advantages. This paper presents a novel pitching style recognition approach for automatic generation of game information and video annotation. First, an effective object segmentation algorithm is designed to compute the body contour and extract the pitcher's body. Then, star skeleton is used as the representative descriptor of the pitcher posture for pitching style recognition. The proposed approach has been tested on broadcast baseball video and the promising experimental results validate the robustness and practicability.
HMM-based ball hitting event exploration system for broadcast baseball video
(2012-07-01)
Chen, Hua-Tsung
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Chou, Chien-Li
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Tsai, Wei-Chin
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Lee, Suh-Yin
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Lin, Bao-Shuh P.
With the dramatic growth of fandom population, a considerable amount of research efforts have been devoted to baseball video processing. However, little work focuses on the detailed follow-ups of ball hitting events. This paper proposes a HMM-based ball hitting event exploration system for broadcast baseball video. Utilizing the strictly-defined layout of the baseball field, the proposed system first detects the game-specific spatial patterns in the field, such as the field lines, the bases, the pitch mound, etc. Then, the play region the currently camera-focused region of the baseball field is identified for frame type classification. Since the temporal patterns of presenting the game progress follow a prototypical order, we consider the classified frame types as observation symbols and recognize ball hitting events using HMM. Experiments conducted on broadcast baseball video show encouraging results in frame type classification and ball hitting event recognition. Three practical applications, including highlight clip extraction by user-designated query, storyboard construction, and similar event retrieval, are introduced to address the applicability of our system. (C) 2012 Elsevier Inc. All rights reserved.
Improving Golf Swing Skills Using Intelligent Glasses
(2015-01-01)
Chen, Hua-Tsung
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Huang, Tzu-Wei
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Chou, Chien-Li
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Tsai, Hou-Chun
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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.
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
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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.
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
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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.
Multimodal Video-to-Near-Scene Annotation
(2017-02)
Chou, Chien-Li
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Chen, Hua-Tsung
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Lee, Suh-Yin
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交大名義發表
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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.
NEAR-DUPLICATE VIDEO RETRIEVAL AND LOCALIZATION USING PATTERN SET BASED DYNAMIC PROGRAMMING
(2013-01-01)
Chou, Chien-Li
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Chen, Hua-Tsung
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Chen, Yi-Cheng
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Ho, Chien-Peng
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Lee, Suh-Yin
With the exponential growth of the web multimedia contents, illegal video copies are widespread and easy to be obtained from search engines and video sharing websites. For video copyright protection, near-duplicate video retrieval becomes more and more important. In this paper, we proposed a Pattern Set based Dynamic Programming (PSDP) algorithm to retrieve near-duplicate videos efficiently and effectively. In addition, the precise positions of the near-duplicate segments in videos can also be located. To better deal with the slow/fast motion and the dropped frame problem, the Time-shift Pattern set Similarity (TPS) is then applied. Two datasets are used to evaluate the effectiveness and efficiency of the proposed method, and the experimental results show that the proposed method outperforms the compared approaches in terms of precision and execution time in both two datasets.
NEAR-DUPLICATE VIDEO RETRIEVAL BY USING PATTERN-BASED PREFIX TREE AND TEMPORAL RELATION FOREST
(2014-01-01)
Chou, Chien-Li
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Chen, Hua-Tsung
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Hsu, Chun-Chieh
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Ho, Chien-Peng
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Lee, Suh-Yin
With the explosive growth of the social multimedia sharing, copyright protection and search result refinement are always the critical issues for the service operators. To resolve the problems, content-based near-duplicate video retrieval is developed in recent years. In this paper, we construct a condensed Pattern-based Prefix tree (PP-tree) to index the patterns of reference videos for fast retrieval. To calculate how likely a query video and a reference video are near-duplicates, a novel algorithm for discovering the temporal relations among patterns is proposed. Comprehensive experiments on public datasets are conducted to verify the effectiveness and efficiency of the proposed method. Experimental results show that the proposed near-duplicate video retrieval approach outperforms the state-of-the-art approaches in terms of precision, recall, and execution time.
Pattern-Based Near-Duplicate Video Retrieval and Localization on Web-Scale Videos
(2015-03-01)
Chou, Chien-Li
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Chen, Hua-Tsung
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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
With the exponential growth of web multimedia contents, the Internet is rife with near-duplicate videos, the video copies applied with visual/temporal transformations and/or post productions. Two critical issues, copyright infringement and search result redundancy, arise accordingly. To resolve these problems, this paper proposes a spatiotemporal pattern-based approach under the hierarchical filter-and-refine framework for efficient and effective near-duplicate video retrieval and localization. Firstly, non-near-duplicate videos are fast filtered out through a computationally efficient data structure, termed pattern-based index tree (PI-tree). Then, an m-pattern-based dynamic programming (mPDP) algorithm is designed to localize near-duplicate segments and to re-rank the videos retrieved. The influence of time shift misalignment can be alleviated by time-shift m-pattern similarity (TPS) measurement. Comprehensive experiments on the five datasets are conducted to verify the effectiveness, efficiency, robustness, and scalability of the proposed approach. Convincing results demonstrate that our proposed approach outperforms the state-of-the-art approaches in terms of mean average precision (MAP) and normalized detection cost rate (NDCR) on the testing datasets. Furthermore, the proposed approach can achieve high quality of near-duplicate video localization in terms of quality frames (QF) and mean F1.
Recognizing tactic patterns in broadcast basketball video using player trajectory
(2012-08-01)
Chen, Hua-Tsung
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Chou, Chien-Li
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Fu, Tsung-Sheng
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Lee, Suh-Yin
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Lin, Bao-Shuh P.
"The explosive growth of the sports fandom inspires much research on manifold sports video analyses and applications. The audience, sports fans, and even professionals require more than traditional highlight extraction or semantic summarization. Computer-assisted sports tactic analysis is inevitably in urging demand. Recognizing tactic patterns in broadcast basketball video is a challenging task due to its complicated scenes, varied camera motion, frequently occlusions between players, etc. In basketball games, the action screen means that an offensive player perform a blocking move via standing beside or behind a defender for freeing a teammate to shoot, to receive a pass, or to drive in for scoring. In this paper, we propose a screen-strategy recognition system capable of detecting and classifying screen patterns in basketball video. The proposed system automatically detects the court lines for camera calibration, tracks players, and discriminates the offensive/defensive team. Player trajectories are calibrated to the real-world court model for screen pattern recognition. Our experiments on broadcast basketball videos show promising results. Furthermore, the extracted player trajectories and the recognized screen patterns visualized on a court model indeed assist the coach/players or the fans in comprehending the tactics executed in basketball games informatively and efficiently. (C) 2012 Elsevier Inc. All rights reserved."
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
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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.
SERVE RECEIVE-TO-ATTACK PERIOD EXTRACTION AND HISTOGRAM-BASED PLAYER LOCALIZATION IN BROADCAST VOLLEYBALL VIDEOS
(2013)
Hsu, Chun-Chieh
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Chen, Hua-Tsung
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Chou, Chien-Li
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Lee, Suh-Yin
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Ho, Chien-Peng
Serve receive-to-attack (SR2A) is the most principal way to gain points in volleyball games. In addition, the positions of players on the court reveal informative clues about both offensive and defensive formations. Hence, in this paper, we propose an effective system capable of extracting SR2A periods from long broadcast volleyball videos (taken from a pan-tilt-zoom camera), and then locating the players using a novel histogram-based approach. Enriched visual presentation can be provided to give the audience or professional players/coaches a further sight into the game. The proposed system consists of four major processing modules, including court detection, SR2A period extraction, camera calibration, and histogram-based player localization. The experiments conducted on broadcast volleyball videos show promising results.
Social Photo Tagging Recommendation Using Community-Based Group Associations
(2012)
Chou, Chien-Li
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Chean, Yee-Choy
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Chen, Yi-Cheng
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Chen, Hua-Tsung
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Lee, Suh-Yin
In the social network, living photos occupy a large portion of web contents. For sharing a photo with the people appearing in that, users have to manually tag the people with their names, and the social network system links the photo to the people immediately. However, tagging the photos manually is a time-consuming task while people take thousands of photos in their daily life. Therefore, more and more researchers put their eyes on how to recommend tags for a photo. In this paper, our goal is to recommend tags for a query photo with one tagged face. We fuse the results of face recognition and the user's relationships obtained from social contexts. In addition, the Community-Based Group Associations, called CBGA, is proposed to discover the group associations among users through the community detection. Finally, the experimental evaluations show that the performance of photo tagging recommendation is improved by combining the face recognition and social relationship. Furthermore, the proposed framework achieves the high quality for social photo tagging recommendation.
SPEED-ADAPTIVE STREET VIEW IMAGE GENERATION USING DRIVING VIDEO RECORDER
(2016)
Chen, Hua-Tsung
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Eddy, Devi
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Chen, Ruei-Lin
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Chou, Chien-Li
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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.