TGIF!: Selecting the most healing TNT by optical flow

In this paper, we propose TNT Gained In optical Flows (TGIF), a new algorithm to select the most entertaining TNT explosions in an Angry Birds-like action puzzle game. We assume that spectators like a high amount of object movement distributed equally over both space and time and that such movement entertains spectators. We, hence, first divide a game video into multiple frames and estimate the optical flows of each frame. With these optical flows, we compute a total displacement and two kinds of Shannon entropy: spatial entropy and temporal entropy. Spatial TGIF and Temporal TGIF are computed by multiplying the total displacement and the respective entropy. We then predict the best explosion video using these two methods. Our results show that the proposed Spatial TGIF’s correct rate is the highest, i.e., 95% which is much higher than 65% by our previous work.

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