TennisSense: a platform for extracting semantic information from multi-camera tennis data

In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface

An automatic visual analysis system for tennis

This article presents a novel video analysis system for coaching tennis players of all levels, which uses computer vision algorithms to automatically edit and index tennis videos into meaningful annotations. Existing tennis coaching software lacks the ability to automatically index a tennis match into key events, and therefore, a coach who uses existing software is burdened with time-consuming manual video editing. This work aims to explore the effectiveness of a system to automatically detect tennis events. A secondary aim of this work is to explore the bene- fits coaches experience in using an event retrieval system to retrieve the automatically indexed events. It was found that automatic event detection can significantly improve the experience of using video feedback as part of an instructional coaching session. In addition to the automatic detection of key tennis events, player and ball movements are automati- cally tracked throughout an entire match and this wealth of data allows users to find interesting patterns in play. Player and ball movement information are integrated with the automatically detected tennis events, and coaches can query the data to retrieve relevant key points during a match or analyse player patterns that need attention. This coaching software system allows coaches to build advanced queries, which cannot be facilitated with existing video coaching solutions, without tedious manual indexing. This article proves that the event detection algorithms in this work can detect the main events in tennis with an average precision and recall of 0.84 and 0.86, respectively, and can typically eliminate man- ual indexing of key tennis events

Development of an Active Vision System for the Remote Identification of Multiple Targets

This thesis introduces a centralized active vision system for the remote identification of multiple targets in applications where the targets may outnumber the active system resources. Design and implementation details of a modular active vision system are presented, from which a prototype has been constructed. The system employs two different, yet complimentary, camera technologies. Omnidirectional cameras are used to detect and track targets at a low resolution, while perspective cameras mounted to pan-tilt stages are used to acquire high resolution images suitable for identification. Five greedy-based scheduling policies have been developed and implemented to manage the active system resources in an attempt to achieve optimal target-to-camera assignments. System performance has been evaluated using both simulated and real-world experiments under different target and system configurations for all five scheduling policies. Parameters affecting performance that were considered include: target entry conditions, congestion levels, target to camera speeds, target trajectories, and number of active cameras. An overall trend in the relative performance of the scheduling algorithms was observed. The Least System Reconfiguration and Future Least System Reconfiguration scheduling policies performed the best for the majority of conditions investigated, while the Load Sharing and First Come First Serve policies performed the poorest. The performance of the Earliest Deadline First policy was seen to be highly dependent on target predictability

Development of an Active Vision System for the Remote Identification of Multiple Targets

This thesis introduces a centralized active vision system for the remote identification of multiple targets in applications where the targets may outnumber the active system resources. Design and implementation details of a modular active vision system are presented, from which a prototype has been constructed. The system employs two different, yet complimentary, camera technologies. Omnidirectional cameras are used to detect and track targets at a low resolution, while perspective cameras mounted to pan-tilt stages are used to acquire high resolution images suitable for identification. Five greedy-based scheduling policies have been developed and implemented to manage the active system resources in an attempt to achieve optimal target-to-camera assignments. System performance has been evaluated using both simulated and real-world experiments under different target and system configurations for all five scheduling policies. Parameters affecting performance that were considered include: target entry conditions, congestion levels, target to camera speeds, target trajectories, and number of active cameras. An overall trend in the relative performance of the scheduling algorithms was observed. The Least System Reconfiguration and Future Least System Reconfiguration scheduling policies performed the best for the majority of conditions investigated, while the Load Sharing and First Come First Serve policies performed the poorest. The performance of the Earliest Deadline First policy was seen to be highly dependent on target predictability

Automatic camera selection for activity monitoring in a multi-camera system for tennis

In professional tennis training matches, the coach needs to be able to view play from the most appropriate angle in order to monitor players' activities. In this paper, we describe and evaluate a system for automatic camera selection from a network of synchronised cameras within a tennis sporting arena. This work combines synchronised video streams from multiple cameras into a single summary video suitable for critical review by both tennis players and coaches. Using an overhead camera view, our system automatically determines the 2D tennis-court calibration resulting in a mapping that relates a player's position in the overhead camera to their position and size in another camera view in the network. This allows the system to determine the appearance of a player in each of the other cameras and thereby choose the best view for each player via a novel technique. The video summaries are evaluated in end-user studies and shown to provide an efficient means of multi-stream visualisation for tennis player activity monitoring

Veliki nadzorni sustav: detekcija i praćenje sumnjivih obrazaca pokreta u prometnim gužvama

The worldwide increasing sentiment of insecurity gave birth to a new era, shaking thereby the intelligent video-surveillance systems design and deployment. The large-scale use of these means has prompted the creation of new needs in terms of analysis and interpretation. For this purpose, behavior recognition and scene understanding related applications have become more captivating to a significant number of computer vision researchers, particularly when crowded scenes are concerned. So far, motion analysis and tracking remain challenging due to significant visual ambiguities, which encourage looking into further keys. By this work, we present a new framework to recognize various motion patterns, extract abnormal behaviors and track them over a multi-camera traffic surveillance system. We apply a density-based technique to cluster motion vectors produced by optical flow, and compare them with motion pattern models defined earlier. Non-identified clusters are treated as suspicious and simultaneously tracked over an overlapping camera network for as long as possible. To aiming the network configuration, we designed an active camera scheduling strategy where camera assignment was realized via an improved Weighted Round-Robin algorithm. To validate our approach, experiment results are presented and discussed.Širom svijeta rasprostranjeni osjećaj nesigurnosti postavio je temelje za dizajniranje i implementaciju inteligentnih sustava nadzora. Velika upotreba ovih sredstava potaknula je stvaranje novih potreba analize i interpretacije. U ovu svrhu, prepoznavanje ponašanja i razumijevanje prizora postaju sve privlačnije povezane primjene značajnom broju istraživača računalne vizije, posebno kada se radi o vrlo prometnim prizorima. Analiza pokreta i slijeđenja ostalo je izazovno područje zbog značajnih vizualnih nejasnoća koje zahtijevaju daljnja istraživanja. U radu je prikazan novi okvir za prepoznavanje različitih uzoraka pokreta, izoliranje neprirodnih ponašanja i njihovo praćenje pomoću nadzornog sustava prometa s više kamera. Primjenjuje se na gustoći zasnovana tehnika skupa vektora pokreta sastavljenih iz optičkog toka te uspoređenih s ranije definiranim modelima uzoraka. Neidentificirani skupovi tretiraju se kao sumnjivi i istovremeno su praćeni mrežom s više preklapajućih kamera što je duže moguće. S ciljem konfiguriranja mreže, dizajnirana je strategija raspoređivanja aktivnih kamera gdje je dodjela kamere ostvarena pomoću unaprijeđenog "Weighted Round-Robin" algoritma

Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras

Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use multiple stages where object detection and localization are performed separately from the control of the PTZ mechanisms. These approaches require manual labels and suffer from performance bottlenecks due to error propagation across the multi-stage flow of information. The large size of object detection neural networks also makes prior solutions infeasible for real-time deployment in resource-constrained devices. We present an end-to-end deep reinforcement learning (RL) solution called Eagle to train a neural network policy that directly takes images as input to control the PTZ camera. Training reinforcement learning is cumbersome in the real world due to labeling effort, runtime environment stochasticity, and fragile experimental setups. We introduce a photo-realistic simulation framework for training and evaluation of PTZ camera control policies. Eagle achieves superior camera control performance by maintaining the object of interest close to the center of captured images at high resolution and has up to 17% more tracking duration than the state-of-the-art. Eagle policies are lightweight (90x fewer parameters than Yolo5s) and can run on embedded camera platforms such as Raspberry PI (33 FPS) and Jetson Nano (38 FPS), facilitating real-time PTZ tracking for resource-constrained environments. With domain randomization, Eagle policies trained in our simulator can be transferred directly to real-world scenarios.Comment: 20 pages, IoTD

Multiple Views Tracking of Maritime Targets

This paper explores techniques for multiple views target tracking in a maritime environment using a mobile surveillance platform. We utilise an omnidirectional camera to capture full spherical video and use an Inertial Measurement Unit (IMU) to estimate the platform?s ego-motion. For each target a part of the omnidirectional video is extracted, forming a corresponding set of virtual cameras. Each target is then tracked using a dynamic template matching method and particle filtering. Its predictions are then used to continuously adjust the orientations of the virtual cameras, keeping a lock on the targets. We demonstrate the performance of the application in several real-world maritime settings

A practical approach for active camera coordination based on a fusion-driven multi-agent system

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02 and CAM CONTEXTS (S2009/TIC-1485).Publicad