Game Plan: What AI can do for Football, and What Football can do for AI

The rapid progress in artificial intelligence (AI) and machine learning has opened unprecedented analytics possibilities in various team and individual sports, including baseball, basketball, and tennis. More recently, AI techniques have been applied to football, due to a huge increase in data collection by professional teams, increased computational power, and advances in machine learning, with the goal of better addressing new scientific challenges involved in the analysis of both individual players’ and coordinated teams’ behaviors. The research challenges associated with predictive and prescriptive football analytics require new developments and progress at the intersection of statistical learning, game theory, and computer vision. In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research, while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come. We illustrate that this duality makes football analytics a game changer of tremendous value, in terms of not only changing the game of football itself, but also in terms of what this domain can mean for the field of AI. We review the state-of-theart and exemplify the types of analysis enabled by combining the aforementioned fields, including illustrative examples of counterfactual analysis using predictive models, and the combination of game-theoretic analysis of penalty kicks with statistical learning of player attributes. We conclude by highlighting envisioned downstream impacts, including possibilities for extensions to other sports (real and virtual)

AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning

WiFi sensing technology has shown superiority in smart homes among various sensors for its cost-effective and privacy-preserving merits. It is empowered by Channel State Information (CSI) extracted from WiFi signals and advanced machine learning models to analyze motion patterns in CSI. Many learning-based models have been proposed for kinds of applications, but they severely suffer from environmental dependency. Though domain adaptation methods have been proposed to tackle this issue, it is not practical to collect high-quality, well-segmented and balanced CSI samples in a new environment for adaptation algorithms, but randomly-captured CSI samples can be easily collected. {\color{black}In this paper, we firstly explore how to learn a robust model from these low-quality CSI samples, and propose AutoFi, an annotation-efficient WiFi sensing model based on a novel geometric self-supervised learning algorithm.} The AutoFi fully utilizes unlabeled low-quality CSI samples that are captured randomly, and then transfers the knowledge to specific tasks defined by users, which is the first work to achieve cross-task transfer in WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for evaluation. The AutoFi transfers knowledge from randomly collected CSI samples into human gait recognition and achieves state-of-the-art performance. Furthermore, we simulate cross-task transfer using public datasets to further demonstrate its capacity for cross-task learning. For the UT-HAR and Widar datasets, the AutoFi achieves satisfactory results on activity recognition and gesture recognition without any prior training. We believe that the AutoFi takes a huge step toward automatic WiFi sensing without any developer engagement.Comment: The paper has been accepted by IEEE Internet of Things Journa

When Moneyball Meets the Beautiful Game: A Predictive Analytics Approach to Exploring Key Drivers for Soccer Player Valuation

To measure the market value of a professional soccer (i.e., association football) player is of great interest to soccer clubs. Several gaps emerge from the existing soccer transfer market research. Economics literature only tests the underlying hypotheses between a player’s market value or wage and a few economic factors. Finance literature provides very theoretical pricing frameworks. Sports science literature uncovers numerous pertinent attributes and skills but gives limited insights into valuation practice. The overarching research question of this work is: what are the key drivers of player valuation in the soccer transfer market? To lay the theoretical foundations of player valuation, this work synthesizes the literature in market efficiency and equilibrium conditions, pricing theories and risk premium, and sports science. Predictive analytics is the primary methodology in conjunction with open-source data and exploratory analysis. Several machine learning algorithms are evaluated based on the trade-offs between predictive accuracy and model interpretability. XGBoost, the best model for player valuation, yields the lowest RMSE and the highest adjusted R2. SHAP values identify the most important features in the best model both at a collective level and at an individual level. This work shows a handful of fundamental economic and risk factors have more substantial effect on player valuation than a large number of sports science factors. Within sports science factors, general physiological and psychological attributes appear to be more important than soccer-specific skills. Theoretically, this work proposes a conceptual framework for soccer player valuation that unifies sports business research and sports science research. Empirically, the predictive analytics methodology deepens our understanding of the value drivers of soccer players. Practically, this work enhances transparency and interpretability in the valuation process and could be extended into a player recommender framework for talent scouting. In summary, this work has demonstrated that the application of analytics can improve decision-making efficiency in player acquisition and profitability of soccer clubs

Real Time Fusion of Radioisotope Direction Estimation and Visual Object Tracking

Research into discovering prohibited nuclear material plays an integral role in providing security from terrorism. Although many diverse methods contribute to defense, there exists a capability gap in localizing moving sources. This thesis introduces a real time radioisotope tracking algorithm assisted by visual object tracking methods to fill the capability gap. The proposed algorithm can estimate carrier likelihood for objects in its field of view, and is designed to assist a pedestrian agent wearing a backpack detector. The complex, crowd-filled, urban environments where this algorithm must function combined with the size and weight limitations of a pedestrian system makes designing a functioning algorithm challenging.The contribution of this thesis is threefold. First, a generalized directional estimator is proposed. Second, two state-of-the-art visual object detection and visual object tracking methods are combined into a single tracking algorithm. Third, those outputs are fused to produce a real time radioisotope tracking algorithm. This algorithm is designed for use with the backpack detector built by the IDEAS for WIND research group. This setup takes advantage of recent advances in detector, camera, and computer technologies to meet the challenging physical limitations.The directional estimator operates via gradient boosting regression to predict radioisotope direction with a variance of 50 degrees when trained on a simple laboratory dataset. Under conditions similar to other state-of-the-art methods, the accuracy is comparable. YOLOv3 and SiamFC are chosen by evaluating advanced visual tracking methods in terms of speed and efficiency across multiple architectures, and in terms of accuracy on datasets like the Visual Object Tracking (VOT) Challenge and Common Objects in Context (COCO). The resultant tracking algorithm operates in real time. The outputs of direction estimation and visual tracking are fused using sequential Bayesian inference to predict carrier likelihood. Using lab trials evaluated by hand on visual and nuclear data, and a synthesized challenge dataset using visual data from the Boston Marathon attack, it can be observed that this prototype system advances the state-of-the-art towards localization of a moving source

MadEye: Boosting Live Video Analytics Accuracy with Adaptive Camera Configurations

Camera orientations (i.e., rotation and zoom) govern the content that a camera captures in a given scene, which in turn heavily influences the accuracy of live video analytics pipelines. However, existing analytics approaches leave this crucial adaptation knob untouched, instead opting to only alter the way that captured images from fixed orientations are encoded, streamed, and analyzed. We present MadEye, a camera-server system that automatically and continually adapts orientations to maximize accuracy for the workload and resource constraints at hand. To realize this using commodity pan-tilt-zoom (PTZ) cameras, MadEye embeds (1) a search algorithm that rapidly explores the massive space of orientations to identify a fruitful subset at each time, and (2) a novel knowledge distillation strategy to efficiently (with only camera resources) select the ones that maximize workload accuracy. Experiments on diverse workloads show that MadEye boosts accuracy by 2.9-25.7% for the same resource usage, or achieves the same accuracy with 2-3.7x lower resource costs.Comment: 19 pages, 16 figure

Understanding cities with machine eyes: A review of deep computer vision in urban analytics

Modelling urban systems has interested planners and modellers for decades. Different models have been achieved relying on mathematics, cellular automation, complexity, and scaling. While most of these models tend to be a simplification of reality, today within the paradigm shifts of artificial intelligence across the different fields of science, the applications of computer vision show promising potential in understanding the realistic dynamics of cities. While cities are complex by nature, computer vision shows progress in tackling a variety of complex physical and non-physical visual tasks. In this article, we review the tasks and algorithms of computer vision and their applications in understanding cities. We attempt to subdivide computer vision algorithms into tasks, and cities into layers to show evidence of where computer vision is intensively applied and where further research is needed. We focus on highlighting the potential role of computer vision in understanding urban systems related to the built environment, natural environment, human interaction, transportation, and infrastructure. After showing the diversity of computer vision algorithms and applications, the challenges that remain in understanding the integration between these different layers of cities and their interactions with one another relying on deep learning and computer vision. We also show recommendations for practice and policy-making towards reaching AI-generated urban policies