Browsing by Author "Chen, Hua-Tsung"

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1-D Integral Image for Enhancing Efficiency and Effectiveness of Probabilistic Occupancy Map-Based People Localization Approach
(2016)
Lin, Yen-Shuo
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Chen, Hua-Tsung
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Hwang, Jenq-Neng
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Hsiao, Ching-Ju
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Chuang, Jen-Hui
The popularity of vision-based surveillance systems arouses much research attention in improving the accuracy and efficiency of people localization. Using probabilistic occupancy map (POM) becomes one of the mainstream approaches to people localization due to its great localization accuracy under severe occlusions and lighting changes. However, to enable the usage of rectangular human models and the subsequent 2-D integral image computation, it is assumed that videos are taken at head or eye level. Even so, the computation complexity is still high. Moreover, surveillance videos are often taken from security cameras located at a higher-up location with an oblique viewing angle, so that human models may be quadrilateral and the pixel-based 2-D integral image cannot be utilized. Accordingly, we propose the use of 1-D integral images which are produced for foreground object(s) in an image along equally-spaced line samples originated from the vanishing point of vertical lines (VPVL). Experimental results show that the proposed approach does improve the efficiency and effectiveness of the POM approach in more general camera configurations.
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 baseball exploration system using spatial pattern recognition
(2008)
Chen, Hua-Tsung
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Hsiao, Ming-Ho
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Chen, Hsuan-Shen
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Tsai, Wen-Jiin
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Lee, Suh-Yin
Despite a lot of research efforts in baseball video processing, little work has been done in analyzing the detailed process and ball movement of the batting content. This paper proposes a novel system to automatically summarize the progress of each batting in baseball videos. Utilizing the strictly-defined specifications of the baseball field, the system recognizes the spatial patterns in each frame and identifies what region of the baseball field is currently focused. Finally, an annotation string which abstracts the batting content is generated. With the annotation strings, the system is able to make descriptions and provide exploration for baseball videos, so that users can be given a further insight into the game quickly. The experiments on broadcast baseball videos of MLB and JPB show promising results.
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.
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
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Lee, Suh-Yin
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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 tempo analysis system for automatic musk accompaniment
(2007)
Chen, Hua-Tsung
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Hsiao, Ming-Ho
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Tsai, Wen-Jiin
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Lee, Suh-Yin
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Yu, Jen-Yu
This paper proposes a music accompaniment system capable of catching the tempo of music. The original signal is first reduced to a detection function revealing the pulses of music. To induce the tempo and locate beats, a Fourier analysis-based algorithm with high practicality and generality is designed that it is robust to beat strength and not restricted to specific music genres. The regularity and periodicity of music beats are extracted and even the missing beats are recovered. The performance is validated using a comprehensive testing data set, and results in both formal objective experiments and subjective listening evaluations show convincible performance. Moreover, an interactive interface is designed that users are allowed to select an instrument to accompany the music based on the obtained tempo.
A trajectory-based ball tracking framework with visual enrichment for broadcast baseball videos
(2008-01-01)
Chen, Hua-Tsung
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Chen, Hsuan-Sheng
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Hsiao, Ming-Ho
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Tsai, Wen-Jiin
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Lee, Suh-Yin
Pitching contents play the key role in the resultant victory or defeat in a baseball game. Utilizing the physical characteristic of ball motion, this paper presents a trajectory-based framework for automatic ball tracking and pitching evaluation in broadcast baseball videos. The task of ball detection and tracking in broadcast baseball videos is very challenging because in video frames, the noises may cause many ball-like objects, the ball size is small, and the ball may deform due to its high speed movement. To overcome these challenges, we first define a set of filters to prune most non-ball objects but retain the ball, even if it is deformed. In ball position prediction and trajectory extraction, we analyze the 2D distribution of ball candidates and exploit the characteristic that the ball trajectory presents in a near parabolic curve in video frames. Most of the non-qualified trajectories are pruned, which greatly improves the computational efficiency. The missed balls can also be recovered in the trajectory by applying the position prediction. The experiments of ball tracking on the testing sequences of JPB, MLB and CPBL captured from different TV channels show promising results. The ball tracking framework is able to extract the ball trajectory, superimposed on the video, and in near real-time provide visual enrichment before the next pitch coming up without specific cameras or equipments set up in the stadiums. It can also be utilized in strategy analysis and intelligence statistics for player training.
Accelerating Vanishing Point-Based Line Sampling Scheme for Real-Time People Localization
(2017-03-01)
Liu, Chin-Wei
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Chen, Hua-Tsung
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Lo, Kuo-Hua
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Wang, Chih-Jung
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Chuang, Jen-Hui
In advanced video surveillance systems, people localization is usually a part of the complete system and should be accomplished in a short time so as to reserve sufficient processing time for subsequent high-level analysis, such as abnormal event/ behavior detection and intruder detection. Hence, in addition to localization accuracy, computational efficiency is of critical importance as well. In this paper, we adopt a vanishing point-based line sampling scheme and propose a fast multicamera people localization approach capable of locating a crowd of dense people and estimating their heights in a fairly short time with high accuracy. For each camera view, sample lines, originated from a vanishing point, of foreground objects are projected onto the ground plane. Then, people locations are estimated by detecting the ground regions containing a high density of the projected lines. Enhanced from some previous works, the proposed approach does not require processing steps of high computation cost, such as projecting all foreground pixels of all views to multiple reference planes or computing pairwise intersections of projected sample lines at different heights. In addition, some novel acceleration modules, such as torso validation and physical rule-based filtering, are developed to further reduce the computation time of people localization. The experiments on real surveillance scenes validate that the proposed approach achieves significant speedup (up to 186%) over state-of-the-art techniques while still ensure a comparably high localization accuracy, even for crowded scenes with serious occlusions.
Acceleration of Vanishing Point-Based Line Sampling Scheme for People Localization and Height Estimation via 3D Line Sampling
(2012-01-01)
Lo, Kuo-Hua
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Wang, Chih-Jung
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Chuang, Jen-Hui
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Chen, Hua-Tsung
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資訊工程學系
With the popularity of vision-based camera surveillance, the research on people localization appeals to much attention. In this paper, we propose an efficient and effective system capable of locating a crowd of dense people in real time, using multiple cameras. For each camera view, sample lines, originated from a vanishing point, of foreground objects are projected on the ground plane. Ground regions containing a high density of projected lines are then used to find people locations. Enhanced from previous works, the people localization approach proposed in this paper needs not project all foreground pixels of all views to multiple reference planes or compute pairwise intersections of projected sample lines at different heights, resulting in significant improvement in computational efficiency. Furthermore, the people heights can also be estimated. Experimental results on real surveillance scenes show that comparable accuracy in people localization can be achieved with five times in computing speed compared with our previous approach.
An Efficient Probabilistic Occupancy Map-Based People Localization Approach
(2015-01-01)
Lin, Yen-Shuo
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Chen, Hua-Tsung
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Chuang, Jen-Hui
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交大名義發表
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資訊工程學系
The widespread use of vision-based video surveillance systems has inspired many research efforts on people localization. One of the current main trends in this field is based on probabilistic occupancy map (POM) obtained from multiple camera views. Although the POM-based approaches are robust against noisy foregrounds and can achieve great localization accuracy, they require high computation complexity. In this paper, two enhancement schemes are proposed to improve the efficiency of the POM-based people localization: (i) quick screening of potential people locations, and (ii) timely termination of iterations for occupancy probability estimation. Experimental results show that the proposed approach achieves up to 7.25 times speed-up compared to the standard POM-based approach, while delivering comparable people localization accuracy.
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.
Ball tracking and 3D trajectory approximation with applications to tactics analysis from single-camera volleyball sequences
(2012-10-01)
Chen, Hua-Tsung
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Tsai, Wen-Jiin
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Lee, Suh-Yin
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Yu, Jen-Yu
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資訊工程學系
Providing computer-assisted tactics analysis in sports is a growing trend. This paper presents an automatic system for ball tracking and 3D trajectory approximation from single-camera volleyball sequences as well as demonstrates several applications to tactics analysis. Ball tracking in volleyball video has great complexity due to the high density of players on the court and the complicated overlapping of ball-player. The 2D-to-3D inference is intrinsically challenging due to the loss of 3D information in projection to 2D frames. To overcome these challenges, we propose a two-phase ball tracking algorithm in which we first detect ball candidates for each frame, and then use them to compute the ball trajectories. With the aid of camera calibration, we involve physical characteristics of ball motion to approximate the 3D ball trajectory from the 2D trajectory. The visualization of 3D trajectory and the applications to trajectory-based tactics analysis not only assist the coaches and players in game study but also make game watching a whole new experience. The experiments on international volleyball games show encouraging results. We believe that the proposed framework can be extended and applied to various kinds of sports games.
Baseball Event Semantic Exploring System Using HMM
(2011)
Tsai, Wei-Chin
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Chen, Hua-Tsung
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Gu, Hui-Zhen
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Lee, Suh-Yin
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Yu, Jen-Yu
Despite a lot of research efforts in baseball video processing in recent years, little work has been done in analyzing the detailed semantic baseball event detection. This paper presents an effective and efficient baseball event classification system for broadcast baseball videos. Utilizing the specifications of the baseball field and the regularity of shot transition, the system recognizes highlight in video clips and identifies what semantic baseball event of the baseball clips is currently proceeding. First, a video is segmented into several highlights starting with a PC (Pitcher and Catcher) shot and ending up with some specific shots. Before every baseball event classifier is designed, several novel schemes including some specific features such as soil percentage and objects extraction such as first base are applied. The extracted mid-level cues are used to develop baseball event classifiers based on an HMM (Hidden Markov model). Due to specific features detection, more hitting baseball events are detected and the simulation results show that the classification of twelve significant baseball events is very promising.
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 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
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Tsai, Wen-Jiin
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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.
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.
Computer-assisted yoga training system
(2018-09-01)
Chen, Hua-Tsung
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He, Yu-Zhen
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Hsu, Chun-Chieh
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資訊工程學系
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Department of Computer Science
Self-training is essential in sports exercise. However, without the instruction of a coach, a practitioner may progress to a limited extent. Improper postures may even 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, which aims at instructing the practitioner to perform yoga poses correctly, assisting in rectifying poor postures, and preventing injury. Integrating computer vision techniques, the proposed system analyzes the practitioner's posture from both front and side views by extracting the body contour, skeleton, dominant axes, and feature points. Then, based on the domain knowledge of yoga training, visualized instructions for posture rectification are presented so that the practitioner can easily understand how to adjust his/her posture. Experiments on twelve yoga poses performed by different practitioners validate the feasibility of the proposed system in yoga training.
Content-aware fast motion estimation algorithm
(2008-05-01)
Chen, Yi-Wen
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Hsiao, Ming-Ho
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Chen, Hua-Tsung
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Liu, Chi-Yu
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Lee, Suh-Yin
In this paper, we propose the Content-Aware Fast Motion Estimation Algorithm (CAFME) that can reduce computation complexity of motion estimation (ME) in H.264/AVC while maintaining almost the same coding efficiency. Motion estimation can be divided into two phases: searching phase and matching phase. In searching phase.. we propose the Simple Dynamic Search Range Algorithm (SDSR) based on video characteristics to reduce the number of search points (SP). In matching phase, we integrate the Successive Elimination Algorithm (SEA) and the integral frame to develop a new SEA for H.264/AVC video compression standard, called Successive Elimination Algorithm with Integral Frame (SEAIF). Besides, we also propose the Early Termination Algorithm (ETA) to early terminate the motion estimation of current block. We implement the proposed algorithm in the reference software JM9.4 of H.264/AVC and the experimental results show that our proposed algorithm can reduce the number of search points about 93.1%, encoding time about 42%, while maintaining almost the same bitrate and PSNR. (c) 2008 Elsevier Inc. All rights reserved.
Content-aware video adaptation under low-bitrate constraint
(2007)
Hsiao, Ming-Ho
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Chen, Yi-Wen
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Chen, Hua-Tsung
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Chou, Kuan-Hung
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Lee, Suh-Yin
With the development of wireless network and the improvement of mobile device capability, video streaming is more and more widespread in such an environment. Under the condition of limited resource and inherent constraints, appropriate video adaptations have become one of the most important and challenging issues in wireless multimedia applications. In this paper, we propose a novel content- aware video adaptation in order to e. effectively utilize resource and improve visual perceptual quality. First, the attention model is derived from analyzing the characteristics of brightness, location, motion vector, and energy features in compressed domain to reduce computation complexity. Then, through the integration of attention model, capability of client device and correlational statistic model, attractive regions of video scenes are derived. The information object- ( IOB-) weighted rate distortion model is used for adjusting the bit allocation. Finally, the video adaptation scheme dynamically adjusts video bitstream in frame level and object level. Experimental results validate that the proposed scheme achieves better visual quality effectively and effciently.
Contour-based strike zone shaping and visualization in broadcast baseball video: providing reference for pitch location positioning and strike/ball judgment
(2010-04-01)
Chen, Hua-Tsung
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Tsai, Wen-Jiin
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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
The strike zone plays a crucial role in baseball. Pitches which pass through the strike zone count as strikes, three of which strike out the batter. Besides strike/ball judgment, the strike zone also provides the reference for positioning pitch locations, about which sports fans and professionals have an intense interest in compiling statistics. This paper presents contour-based strike zone shaping and visualization in broadcast baseball video. We first detect the home plate, which determines the vertical limits of the strike zone. Then, an efficient algorithm is designed for batter contouring and the points of curvature extremes on the batter's contour are computed to locate the dominant points which determine the horizontal limits of the strike zone. The experiments show that the proposed framework is able to shape the strike zone fairly well in various baseball sequences, needless of manual operation and additional camera setting. The proposed strike zone shaping and visualization will be able to assist in pitch analysis and statistics compiling.