Real-time field sports scene classification using colour and frequency space decompositions

This paper presents a novel approach to recognize a scene presented in an image with specific application to scene classification in field sports video. We propose different variants of the algorithm ranging from bags of visual words to the simplified real-time implementation, that takes only the most important areas of similar colour into account. All the variants feature similar accuracy which is comparable to very well-known image indexing techniques like SIFT or HoGs. For the comparison purposes, we also developed a specific database which is now available online. The algorithm is suitable in scene recognition task thanks to changes in speed and robustness to the image resolution, thus, making it a good candidate in real-time video indexing systems. The procedure features high simplicity thanks to the fact that it is based on the very well-known Fourier transform.