Chun-Chieh Hsu

Daytime Preceding Vehicle Brake Light Detection Using Monocular Vision

Advanced vehicle safety is a recently emerging issue appealed from the explosive population of car owners. Increasing driver assistance systems have been developed for warning drivers of potential hazards by analyzing the surroundings with sensors and/or cameras. Issuing vehicle deceleration and potential collision, brake lights are particularly important warning signals, allowing of no neglect. In this paper, we propose a vision-based daytime brake light detection system using a driving video recorder, which tends to be widespread used. At daytime, visual features, motions, and appearances of vehicles are highly visible. However, brake lights, on the contrary, are hard to notice due to low contrast between the brakes lights and environments. Without the significant characteristic of light scattering as at night, the proposed system extracts preceding vehicles with taillight symmetry verification, and then integrates both luminance and radial symmetry features to detect brake lights. A detection refinement process using temporal information is also employed for miss recovery. Experiments are conducted on a test data set collected by front-mounted driving video recorders, and the results verify that the proposed system can effectively detect brake lights at daytime, showing its good feasibility in real-world environments.

Spiking and Blocking Events Detection and Analysis in Volleyball Videos

In volleyball matches, spiking is the most effective way to gain points, while blocking is the action to prevent the opponents from getting scores by spiking. In this paper, we propose an intelligent system for automatic spiking events detection and blocking pattern classification in real volleyball videos. First, the entire videos are segmented into clips of rallies by whistle detection. Then, we find the court region based on proper camera calibration, and detect the location of the net for judging the positions of spiking and blocking. Via analyzing the changes of moving pixels along the net, we make a bounding box around the blocking location, so as to classify the blocking patterns into two main categories based on the width of bounding box. Finally, two important tactic patterns, delayed spiking and alternate position spiking, are recognized. With the information of spiking events and blocking locations, we can collect the statistical data and make tactics inference easily. To the best of our knowledge, no previous work is focused on spiking or blocking event detection. The experimental results on the videos recorded by a university volleyball team are promising and demonstrate the effectiveness of our proposed scheme.

Trajectory Based Jump Pattern Recognition in Broadcast Volleyball Videos

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.

Near-duplicate video retrieval by using pattern-based Prefix tree and temporal relation forest

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.

Serve receive-to-attack period extraction and histogram-based player localization in broadcast volleyball videos

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.

2D Histogram-based player localization in broadcast volleyball videos

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.

Vision-Based Road Bump Detection Using a Front-Mounted Car Camcorder

Advanced vehicle safety is a recently emerging issue, appealed from the rapidly explosive population of car owners. Increasing driver assistance systems have been designed for warning drivers of what should be noticed by analyzing surrounding environments with sensors and/or cameras. As one of the hazard road conditions, road bumps not only damage vehicles but also cause serious danger, especially at night or under poor lighting conditions. In this paper we propose a vision-based road bump detection system using a front-mounted car camcorder, which tends to be widespread deployed. First, the input video is transformed into a time-sliced image, which is a condensed video representation. Consequently, we estimate the vertical motion of the vehicle based on the time-sliced image and infer the existence of road bumps. Once a bump is detected, the location fix obtained from GPS is reported to a central server, so that the other vehicles can receive warnings when approaching the detected bumpy regions. Encouraging experimental results show that the proposed system can detect road bumps efficiently and effectively. It can be expected that traffic security will be significantly promoted through the mutually beneficial mechanism that a driver who is willing to report the bumps he/she meets can receive warnings issued from others as well.

Yoga Posture Recognition for Self-training

Self-training plays an important role in sports exercise, but improper training postures can cause serious harm to muscles and ligaments of the body. Hence, more and more researchers are devoted into the development of computer-assisted self-training systems for sports exercise. In this paper, we propose a Yoga posture recognition system, which is capable of recognizing what Yoga posture the practitioner is performing, and then retrieving Yoga training information from Internet to remind his/her attention to the posture. First, a Kinect is used for capturing the user body map and extracting the body contour. Then, star skeleton, which is a fast skeletonization technique by connecting from centroid of target object to contour extremes, is used as a representative descriptor of human posture for Yoga posture recognition. Finally, some Yoga training information for the recognized posture can be retrieved from Internet to remind the practitioner what to pay attention to when practicing the posture.

Computer-assisted yoga training system

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.

Computer-Assisted Billiard Self-Training Using Intelligent Glasses

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 computerassisted 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.