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

Despite active research and significant progress in the last 30 years, eye detection and tracking remains challenging due to the individuality of eyes, occlusion, variability in scale, location, and light conditions. Data on eye location and details of eye movements have numerous applications and are essential in face detection, biometric identification, and particular human-computer interaction tasks. This paper reviews current progress and state of the art in video-based eye detection and tracking in order to identify promising techniques as well as issues to be further addressed. We present a detailed review of recent eye models and techniques for eye detection and tracking. We also survey methods for gaze estimation and compare them based on their geometric properties and reported accuracies. This review shows that, despite their apparent simplicity, the development of a general eye detection technique involves addressing many challenges, requires further theoretical developments, and is consequently of interest to many other domains problems in computer vision and beyond.

/pdf/in-the-eye-of-the-beholder-a-survey-of-models-for-eyes-and-oxe7gfw5jq.pdf

In the Eye of the Beholder: A Survey of Models for Eyes and Gaze

Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

Person re-identification (re-ID) has become increasingly popular in the community due to its application and research significance. It aims at spotting a person of interest in other cameras. In the early days, hand-crafted algorithms and small-scale evaluation were predominantly reported. Recent years have witnessed the emergence of large-scale datasets and deep learning systems which make use of large data volumes. Considering different tasks, we classify most current re-ID methods into two classes, i.e., image-based and video-based; in both tasks, hand-crafted and deep learning systems will be reviewed. Moreover, two new re-ID tasks which are much closer to real-world applications are described and discussed, i.e., end-to-end re-ID and fast re-ID in very large galleries. This paper: 1) introduces the history of person re-ID and its relationship with image classification and instance retrieval; 2) surveys a broad selection of the hand-crafted systems and the large-scale methods in both image- and video-based re-ID; 3) describes critical future directions in end-to-end re-ID and fast retrieval in large galleries; and 4) finally briefs some important yet under-developed issues.

/pdf/person-re-identification-past-present-and-future-44l6xye7rx.pdf

Person Re-identification: Past, Present and Future

A visual approach for driver inattention detection

A review of vision-based systems for soccer video analysis

A new algorithm for ball recognition using circle Hough transform and neural classifier

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 detection of internal defects in composite materials with non-destructive techniques is an important requirement both for quality checks during the production phase and in-service inspection during maintenance operations. Visual inspection allows only the analysis of surface characteristics of materials and, then, if internal faults occur inside composite structures, a deeper analysis is required. A comparison between the reactions of different materials to ultrasonic signals can be used to highlight the difference in the internal structures and also to detect the depth position of these anomalies. However, ultrasonic data are difficult to interpret since they require the analysis of a continuous signal for each point of the material under consideration. An automatic procedure is necessary to manage large data sets and to extract significant differences between them. In this paper, we address the problem of automatic inspection of composite materials using an ultrasonic technique. We consider two main steps for interpreting ultrasonic data: the pre-processing technique necessary to normalize the signals of composite structures with different thicknesses and the classification techniques used to compare the ultrasonic signals and detect classes of similar points.

Tiziana D'Orazio

Papers

A visual approach for driver inattention detection

A review of vision-based systems for soccer video analysis

A new algorithm for ball recognition using circle Hough transform and neural classifier

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

Automatic ultrasonic inspection for internal defect detection in composite materials