Deepshot

Abstract

The whole area of Football video analysis turned out to be very important for researchers because the necessity of speedy and automated ways of highlight generation is growing rapidly across digital media platforms. The longer the match, the more manual review becomes time-consuming, inconsistent, and therefore unsuitable for large-scale workflows. The project called "DEEPSHOT" presents an intelligent, fully-automated solution that is able to transform raw soccer match recordings into concise highlight reels by integrat ing three main technologies: deep learning, temporal modeling, and multi-stage video analytics. Different from regular systems which heavily depend on the creation of rules and the use of simple motion detection, DEEPSHOT is a machine learning method that is based on deep visual embeddings and the interpretation of temporal sequence. Extraction of frame-level representations is the first step of the system, which is achieved by using a pretrained ResNet-152 backbone, followed by dimension reduction to create compact temporal descriptors for processing long-sequence. After that, the descriptors are processed by a multi-stage temporal modeling pipeline to detect key soccer events like goals, shots, fouls, cards, corners, and substitutions with a very high level of confidence. The system integrates multiple processing units which include: feature extraction, PCA based compression, temporal event scoring, time stamp alignment and automatic highlight assembly to comprise the one workflow. DEEPSHOT not only gives structured JSON event metadata but also uses FFmpeg video automation to create a polished highlight reel without any human participation. The whole system is developed using Python and is also supported by a mixture of well-known libraries like PyTorch, NumPy, SciPy, scikit-learn, OpenCV, which guarantees it to be efficient, scalable, and production-ready. Through the application of deep visual perception and advanced temporal analysis, DEEPSHOT has developed a very powerful and flexible solution for soccer highlight generation. It indeed lessens the manual editing and at the same time maintains the same quality of highlights and demonstrates the real-world applicability of AI video summarization in modern sports media technology