About Ice hockey videos daily for windows 8
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Be able to detect and track the puck regardless of the condition of the puck and retrieve the puck after severe occlusion by players or logos. The proposed approach has two advantages compared to existing techniques: 1) The approach is able to track ice hockey pucks with low texture features, which the existing method, popular single target trackers, fails to do. Prove that it’s possible to incorporate well-developed trackers in ice hockey game analysis. 2) The approach firstly addresses a previously avoided problem of tracking nearly invisible pucks after shooting actions in existing methods to our best knowledge.Lots of previous work addressed detecting and tracking balls, yet most of them target soccer balls [1], [2], basketballs [3], [4], table tennis balls [5], tennis balls [6], etc. Only very few works aim to track the ice hockey puck [7] due to the following difficulties as shown in Fig. 1: (1) Pucks moving at high speed, (2) Lots of similar interference in the rink, (3) Rapidly changing size and shape according to the frame rate of the camera, (4) Low-resolution images, (5) Constantly occluded by players or logos, (6) Illumination changes due to the camera motion.As for the tracking task itself, the great performance of state-of-the-art trackers depends on rich features extracted by handcraft techniques or even deep convolution neural networks. However, when dealing with cases like tracking ice hockey pucks, the extreme lack of appearance features almost determines the failure of state-of-the-art trackers. Thus, how to successfully track ice hockey pucks in broadcast videos still remains a big challenge despite the rapid development of single object tracking.Most of the approaches about detecting and tracking the ball can be classified into three aspects: feature-based, model-based and motion-based. Feature-based approaches take advantage of color histogram, geometry, HoG (Histogram of Oriented Gradient) [8], or other handcraft features [9], [10], [11]. Model-based approaches use semantic representations or domain knowledge [12], [13], [14], while motion-based approaches incorporate the trajectory of balls to discriminate them from other interference or background[1], [5], [15], [16]. All of the approaches mentioned above try to highlight the ball from the rest
