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

This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications; contains numerous step-by-step algorithms; describes a broad range of applications; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.

/pdf/handbook-of-face-recognition-2d5mzdpu08.pdf

Handbook of Face Recognition

In this paper, we present an appearance-based method for person re-identification. It consists in the extraction of features that model three complementary aspects of the human appearance: the overall chromatic content, the spatial arrangement of colors into stable regions, and the presence of recurrent local motifs with high entropy. All this information is derived from different body parts, and weighted opportunely by exploiting symmetry and asymmetry perceptual principles. In this way, robustness against very low resolution, occlusions and pose, viewpoint and illumination changes is achieved. The approach applies to situations where the number of candidates varies continuously, considering single images or bunch of frames for each individual. It has been tested on several public benchmark datasets (ViPER, iLIDS, ETHZ), gaining new state-of-the-art performances.

/pdf/person-re-identification-by-symmetry-driven-accumulation-of-4a0wu8gbtk.pdf

Person re-identification by symmetry-driven accumulation of local features

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

Researchers studying aspects of locomotion or movement in biological and biomimetic systems commonly use video or stereo video recordings to quantify the behaviour of the system in question, often with an emphasis on measures of position, velocity and acceleration. However, despite the apparent simplicity of video analysis, it can require substantial investment of time and effort, even when performed with adequate software tools. This paper reviews the underlying principles of video and stereo video analysis as well as its automation and is accompanied by fully functional and freely available software implementation.

https://iopscience.iop.org/article/10.1088/1748-3182/3/3/034001/pdf

Software techniques for two- and three-dimensional kinematic measurements of biological and biomimetic systems.

The paper investigates the unsupervised learning of a model of activity for a multi-camera surveillance network that can be created from a large set of observations. This enables the learning algorithm to establish links between camera views associated with an activity. The learning algorithm operates in a correspondence-free manner, exploiting the statistical consistency of the observation data. The derived model is used to automatically determine the topography of a network of cameras and to provide a means for tracking targets across the "blind" areas of the network. A theoretical justification and experimental validation of the methods are provided.

Bridging the gaps between cameras

This paper presents a methodology for evaluating the performance of video surveillance tracking systems. We introduce a novel framework for performance evaluation using pseudo-synthetic video, which employs data captured online and stored in a surveillance database. Tracks are automatically selected from the surveillance database and then used to generate ground truthed video sequences with a controlled level of perceptual complexity that can be used to quantitatively characterise the quality of the tracking algorithms.

/pdf/a-novel-method-for-video-tracking-performance-evaluation-bipptgxmdv.pdf

A novel method for video tracking performance evaluation

Multi-camera image measurement and correspondence

We describe a system for visual surveillance for outdoor environments using an intelligent multi camera network. Each intelligent camera uses robust techniques for detecting and tracking moving objects. The system architecture implemented supports the real time capture and storage of object track information into a surveillance database. The tracking data stored in the surveillance database is analysed in order to learn semantic scene models, which describe entry zones, exit zones, links between cameras, and major routes in each camera view. These set of models provide a robust framework for coordinating the tracking of objects between overlapping and nonoverlapping cameras, and recording the activity of objects detected by the system.

Wide area surveillance with a multi camera network

This paper presents a framework for event detection and video content analysis for visual surveillance applications. The system is able to coordinate the tracking of objects between multiple camera views, which may be overlapping or non-overlapping. The key novelty of our approach is that we can automatically learn a semantic scene model for a surveillance region, and have defined data models to support the storage of different layers of abstraction of tracking data into a surveillance database. The surveillance database provides a mechanism to generate video content summaries of objects detected by the system across the entire surveillance region in terms of the semantic scene model. In addition, the surveillance database supports spatio-temporal queries, which can be applied for event detection and notification applications

A hierarchical database for visual surveillance applications

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