Medium Scale Benchmark for Cricket Excited Actions Understanding

Altaf Hussain, Noman Khan, Muhammad Munsif, Min Je Kim, Sung Wook Baik; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3399-3409

The Sports Action Recognition (SAR) domain is of significant importance in research with diverse applications ranging from aiding coaches in strategic decision-making to empowering athletes and contributing to real-time commercial entertainment. Despite the existence of extensive large-scale and small-scale datasets the direct application of these datasets to specific sports domains such as cricket poses challenges. Existing datasets predominantly center around daily life actions lacking the necessary granularity for in-depth sports analyses. Current Cricket Action Analysis (CAA) datasets have limitations including their small scale modality constraints and their narrow focus on specific aspects such as cricket batting. Recognizing the need for a more comprehensive benchmark this article introduces the Cricket Excited Actions (CEA) dataset. Developed in collaboration with professional cricket players the CEA dataset encompasses challenging multi-person actions within realistic cricket scenarios. The selected activity classes such as Clean Bowled Six Four and Catches adhere to official standards and represent pivotal moments in cricket matches. Through precise annotation and empirical studies utilizing state-of-the-art action recognition model architectures this study provides a valuable resource for further research and makes significant contributions by offering support essential to advancing CAA within the cricket sports community. The data and code are available at https://github.com/Altaf-hucn/Cricket-Excited-Actions-Benchmark.