In a sports video, the significant events are caused mostly because of ball-player and playerplayer interaction. To detect and track a ball or a player in a sports video becomes more challenging in presence of many mo...
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In a sports video, the significant events are caused mostly because of ball-player and playerplayer interaction. To detect and track a ball or a player in a sports video becomes more challenging in presence of many moving objects in the background. The small size of the ball in relation to the frame size makes ball detection much more difficult. In addition, the ball-images are also getting deformed due to the high speed movement of the ball. Often the ball gets occluded by players and the ball image gets merged with lines and boarders in the field. In this paper, the problem of ball detection-and-tracking in a real time basketball video is addressed. Here a trajectorybased ball-detection-and-tracking method is proposed to detect and track the ball in a set of videos of basketball long shot sequences. A two-fold detection-and-tracking framework is used where the ball is detected using a feature-based method in the first step. The second step verifies the detection result of the ball detection system using the 2D-trajectory information of the possible ball candidates in the frame. The true ball trajectory is extracted from a set of candidate trajectories and the ball locations are estimated along the trajectory. The missing ball positions due to occlusion of the ball and merging of the ball image with other objects in the background are estimated using a trajectory interpolation technique.
With the dense deployment and wide applications of the Internet of Things in railway systems, the location-based security access control scheme is becoming increasingly important. In this study, the received signal st...
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With the dense deployment and wide applications of the Internet of Things in railway systems, the location-based security access control scheme is becoming increasingly important. In this study, the received signal strength (RSS) and channel state information (CSI) in railway communications are measured by AR9344 network interface card. Then, based on the measurement data, the authors propose trajectory-based, neural network-based (NN-based) and ray tracing-based (RT-based) localisation algorithms, serving for location-based security access control. Specifically, the trajectory-based algorithm combined with trajectory simulation, movement detection and dynamic time warping algorithms, realises passengers enter/exit pattern detection. The NN-basedalgorithm leverages back-propagation network (BPN) and constructs training sets with measurement-based RSS and CSI, finishing accurate localisation. Besides, they evaluate the algorithm performance under different layers of BPN. RT-based localisation algorithm combines measurement data and simulation analysis, leveraging simulated-based multiple-input multiple-output received power and delay spread to realise lightweight localisation. After evaluation, the RT-basedalgorithm can achieve the highest accuracy of localisation, up to 99.9% and is designed to be straightforward for integration with commercial access points and deployment to railway communications.
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