http://cvssp-data.eps.surrey.ac.uk/Personal/AdrianHilton/Publications_files/survey_techrep.pdf

A survey of advances in vision-based human motion capture and analysis

To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2,700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.

/pdf/performance-measures-and-a-data-set-for-multi-target-multi-3aw16a43em.pdf

Performance Measures and a Data Set for Multi-target, Multi-camera Tracking

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Pattern recognition

To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080p, 60fps video taken by 8 cameras observing more than 2,700 identities over 85 minutes; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.

Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking

Moving object segmentation by background subtraction and temporal analysis

The problem of ground truth generation is fundamental for many approaches of computer vision and image processing. In order to test algorithms for object segmentation, object tracking, object interactions, it is necessary to have image sequences in which the ground truth is determined in an objective way. In the context of visual surveillance where many people moves in the scene occluding each other, it could be very complex and hard the work of generating for each image the position of all the moving objects and maintain this information for all the period in which they remain in the scene. In this paper we propose a semi-automatic system that generates an initial ground truth estimation, and then provides a user-friendly interface to manually validate or correct the track results. The proposed system has been tested on some soccer video sequences that have been published on-line for being available to the scientific community, but it can be used also in other surveillance contexts.

A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences

The present invention relates to a system for detecting and classifying events during motion actions, in particular “offside” event in the football game. The system allows determining such event in a real-time and semi-automatic context, by taking into account the variability of the environmental conditions and of the dynamics of the events which can be traced back to the offside. The present invention proposes itself with a not-invasive technique, compatible with the usual course of the match.

Method and system for the detection and the classification of events during motion actions

In this paper, we investigate on the feasibility of multiple camera system for automatic offside detection. We propose six fixed cameras, properly placed on the two sides of the soccer field (three for each side) to reduce perspective and occlusion errors. The images acquired by the synchronized cameras are processed to detect the players' position and the ball position in real-time; a multiple view analysis is carried out to evaluate the offside event, considering the position of all the players in the field, determining the players who passed the ball, and determining if active offside condition occurred. The whole system has been validated using real-time images acquired during official soccer matches, and quantitative results on the system accuracy were obtained comparing the system responses with the ground truth data generated manually on a number of extracted significant sequences.

An Investigation Into the Feasibility of Real-Time Soccer Offside Detection From a Multiple Camera System

People Tracking in multiple cameras is of great interest for wide area video surveillance systems. Multi-camera tracking with non-overlapping fields of view (FOV) involves the tracking of people in the blind region and their correspondence matching across cameras. We consider these problems in this paper. We propose a multi camera architecture for wide area surveillance and a real time people tracking algorithm across non overlapping cameras. We compared different methods to evaluate the color Brightness Transfer Function (BTF) between non overlapping cameras. These approaches are based on a testing phase during which the color histogram mapping, between pairs of images of the same object observed in the different field of views, is carried out. The experimental results compare two different transfer functions and demonstrate their limits in people association when a new person enters in one camera FOV.

Paolo Spagnolo

Papers

Moving object segmentation by background subtraction and temporal analysis

A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences

Method and system for the detection and the classification of events during motion actions

An Investigation Into the Feasibility of Real-Time Soccer Offside Detection From a Multiple Camera System

Color Brightness Transfer Function evaluation for non overlapping multi camera tracking