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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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Author: search.filters.author.Chen, Hua-Tsung

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Now showing 1 - 10 of 54
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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
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    Chou, Chien-Li
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    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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    PANORAMA GENERATION BASED ON AERIAL IMAGES
    (2018-01-01)
    Ye, Jyun-Gu
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    Chen, Hua-Tsung
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    Tsai, Wen-Jiin
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    資訊工程學系
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    Department of Computer Science
    Due to the progress of manufacturing technology of drones and cameras, the prices of camera-equipped drones are not as expensive as ever and can be easily afford by general people. Camera-equipped drones are mainly used for aerial photography, taking pictures from higher positions, and their shooting coverages are usually much larger than those of traditional handheld cameras. In this paper, we propose an aerial panorama generation system using a camera-equipped drone. The aerial scenes along the flight of the drone can be summarized in an aerial panorama. To make the system adaptive to the flying speed of the drone, we propose a novel speed estimation algorithm capable of measuring the distances of pixel movement between consecutive frames. Consequently, the proposed system can generate aerial panoramas properly, in no need of requiring the drone to fly at a constant speed. Experiments conducted on real videos captured by a drone demonstrate that the proposed aerial panorama generation system can produce satisfactory results.
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    DATA AUGMENTATION FOR CNN-BASED PEOPLE DETECTION IN AERIAL IMAGES
    (2018-01-01)
    Chen, Hua-Tsung
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    Liu, Che-Han
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    Tsai, Wen-Jiin
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    資訊工程學系
    ;
    Department of Computer Science
    Much more than ever, many important places have deployed surveillance cameras for early detection of abnormal events and suspects. However, the monitoring ability of fixed cameras is significantly limited due to the low flexibility, blind spot, and obstacle occlusion. With high mobility, drones have high potential for supporting security surveillance. On the other hand, people detection plays a key role in intelligent surveillance system, and increasing deep learning-based methods show great results. However, the training data for aerial images are still few, even though there are many public datasets available. Thus, in this paper we research on data augmentation, try transforming general images to be aerial image-like, and make an attempt to improving the performance of deep learning-based people detection with existing datasets. The experiments conducted on the real aerial images collected by a camera drone show encouraging results.
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    Fast Multi-View People Localization Using a Torso-High Reference Plane
    (2017-01-01)
    Hsu, Chun-Chieh
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    Chen, Hua-Tsung
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    Tsai, Wen-Jiin
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    Lee, Suh-Yin
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    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.
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    PEDESTRIAN DETECTION IN AERIAL IMAGES USING VANISHING POINT TRANSFORMATION AND DEEP LEARNING
    (2018-01-01)
    Chang, Ya-Ching
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    Chen, Hua-Tsung
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    Chuang, Jen-Hui
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    Liao, I-Chun
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    資訊工程學系
    Drones are well-liked nowadays. However, deep learning models for object detection still cannot have high detection rates for pedestrians in aerial images even though they already show high precision on PASCAL VOC 2007. The main challenges of aerial image analysis include: (i) the size of an object in aerial images can be very small, and (ii) the objects in aerial images are tilted outward due to perspective projection deformation, which make the pedestrians hard to recognize in aerial images. In this paper, we utilize image partition and vanishing point transformation to overcome the above challenges. Experimental results demonstrate that such pre-processing methods can indeed increase the detection rates significantly for some deep learning models.
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    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.
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    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.
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    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
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    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.
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    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.
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    DEEP LEARNING-BASED HUMAN ACTIVITY ANALYSIS FOR AERIAL IMAGES
    (2017-01-01)
    Wang, Han-Yang
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    Chang, Ya-Ching
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    Hsieh, Yi-Yu
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    Chen, Hua-Tsung
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    Chuang, Jen-Hui
    Due to the advantages of high mobility and the ability to fly in the sky, drone has inspired more and more applications in recent years. On the other hand, deep learning-based human activity analysis is an important research topic in security surveillance; however, there are few research works on such analysis with aerial images so far. Because of perspective projection, people in aerial images look tilted, which would degrade the performance of human activity analysis. In order to cope with the issue of perspective projection for aerial images, we modify the CNN architecture of a state-ofthe-art object detection method, YOLOv2 [12], and build an aerial image dataset with a drone for new model training. Finally, a post -processing method is proposed to classify the pose of a detected person as normal or abnormal, so that the task of human activity analysis with aerial images can be accomplished.