In this paper of a research based project, using Bidirectional Long Short-Term Memory (BiLSTM) networks, we provide a novel Fight Scene Detection (FSD) model which can be used for Movie Highlight Generation Systems (MHGS) based on deep learning and Neural Networks . Movies usually have Fight Scenes to keep the audience amazed. For trailer generation, or any other application of Highlight generation, it is very tidious to first identify all such scenes manually and then compile them to generate a highlight serving the purpose. Our proposed FSD system utilises temporal characteristics of the movie scenes and thus is capable to automatically identify fight scenes. Thereby helping in the effective production of captivating movie highlights. We observe that the proposed solution features 93.5% accuracy and is higher than 2D CNN with Hough Forests which being 92% accurate and is significantly higher than 3D CNN which features an accuracy of 65%.

基于双向长短期记忆网络(BiLSTM)的研究项目研究了战斗场景检测(FSD)模型，可用于基于深度学习和神经网络的电影精彩片段生成系统(MHGS)。该系统利用电影场景的时间特征能够自动识别战斗场景，提高了引人入胜的电影精彩片段的有效制作。观察发现，该解决方案的准确率为93.5%，高于92%准确率的二维CNN与Hough森林以及65%准确率的三维CNN。

电影精彩片段生成系统中的打斗场景检测