Tracking Multiple Players using a Single Camera

It has been shown that multi-people tracking could be successfullly formulated as a Linear Program to process the output of multiple fixed and synchronized cameras with overlapping fields of view. In this paper, we extend this approach to the more challenging single-camera case and show that it yields excellent performance, even when the camera moves. We validate our approach on a number of basketball matches and argue that using a properly retrained people detector is key to producing the probabilities of presence that are used as input to the Linear Program

Analyzing Volleyball Match Data from the 2014 World Championships Using Machine Learning Techniques

This paper proposes a relational learning based approach for discovering strategies in volleyball matches based on optical tracking data. In contrast to most existing methods, our approach permits discovering patterns that account for both spatial (that is, partial configurations of the players on the court) and temporal , that is, the order of events and positions, aspects of the game. We analyze both the men’s and women’s final match from the 2014 FIVB Volleyball World Championships, and are able to identify several interesting and relevant strategies from the matches

Take your Eyes off the Ball: Improving Ball-Tracking by Focusing on Team Play

Accurate video-based ball tracking in team sports is important for automated game analysis, and has proven very difficult because the ball is often occluded by the players. In this paper, we propose a novel approach to addressing this issue by formulating the tracking in terms of deciding which player, if any, is in possession of the ball at any given time. This is very different from standard approaches that first attempt to track the ball and only then to assign possession. We will show that our method substantially increases performance when applied to long basketball and soccer sequences

Tracking Multiple Handball Players using Multi-Commodity Network Flow for Assessing Tactical Behavior

The aim of this work is to present an approach that can help to characterize teams’ and players’ tactical behavior using two techniques to aid handball coaches to assess tactical procedures when using spatial measures derived from players position data. Results suggest that it is possible to identify tactical spatial differences between fast-break and fast throw-off. The approach presented in this work may be useful to reduce the time spent in game analysis and to improve coaches’ assessment of tactical performance during the training sessions

Conditional Random Fields for Multi-Camera Object Detection

We formulate a model for multi-class object detection in a multi-camera environment. From our knowledge, this is the first time that this problem is addressed taken into account different object classes simultaneously. Given several images of the scene taken from different angles, our system estimates the ground plane location of the objects from the output of several object detectors applied at each viewpoint. We cast the problem as an energy minimization modeled with a Conditional Random Field (CRF). Instead of predicting the presence of an object at each image location independently, we simultaneously predict the labeling of the entire scene. Our CRF is able to take into account occlusions between objects and contextual constraints among them. We propose an effective iterative strategy that renders tractable the underlying optimization problem, and learn the parameters of the model with the max-margin paradigm. We evaluate the performance of our model on several challenging multi-camera pedestrian detection datasets namely PETS 2009 and EPFL terrace sequence. We also introduce a new dataset in which multiple classes of objects appear simultaneously in the scene. It is here where we show that our method effectively handles occlusions in the multi-class case

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an Operating Room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures (3DPS) and 2D body part localization across all camera views, using Convolutional Neural Networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset

Facial Descriptors for Identity-Preserving Multiple People Tracking

Abstract. In this report, we show that facial descriptors can be used very effectively in conjunction with a tracklet-based multi-person tracker both to localize and to identify or re-identify people over long sequences. Thus, we can reliably deliver both trajectories and identities in crowded scenes. Furthermore, the whole approach is fast enough to be implemented in real-time. Our key insight is that this can be done even though the faces can only be recognized relatively infrequently. 1 Both authors have contributed equally to this wor

Tracklet-based Multi-Commodity Network Flow for Tracking Multiple People

(EN)A method for continuously tracking multiple people partitioned into groups while preserving identities under global appearance constraints, wherein people's trajectories may intersect, and wherein only sparse appearance information is available is disclosed. Individual trajectories for each group identity are obtained by solving a layered tracklet-based multi-commodity f low (MCNF) programming problem, wherein tracklets are connected parts of splitted trajectories, wherein each trajectory is split at posit ions which are in the neighborhood of another, wherein said neighborhood encompasses locations within a predefined distance. (FR)L'invention concerne un procédé pour suivre en continu de multiples personnes partitionnées en groupes tout en préservant des identités sous des contraintes d'apparence globale, dans lequel des trajectoires de personnes peuvent se couper, et dans lequel seules des informations d'apparence éparses sont disponibles. Des trajectoires individuelles pour chaque identité de groupe sont obtenues par résolution d'un problème de programmation de flux plurisectoriel, basé sur de mini-trajectoires, en couches (MCNF), les mini-trajectoires étant des parties reliées de trajectoires divisées, chaque trajectoire étant divisée à des positions qui sont au voisinage les unes des autres, ledit voisinage englobant des emplacements dans une distance prédéfinie