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The Visual Object Tracking challenge VOT2021 is the ninth annual tracker benchmarking activity organized by the VOT initiative. Results of 71 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in recent years. The VOT2021 challenge was composed of four sub-challenges focusing on different tracking domains: (i) VOT-ST2021 challenge focused on short-term tracking in RGB, (ii) VOT-RT2021 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2021 focused on long-term tracking, namely coping with target disappearance and reappearance and (iv) VOT-RGBD2021 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2021 dataset was refreshed, while VOT-RGBD2021 introduces a training dataset and sequestered dataset for winner identification. The source code for most of the trackers, the datasets, the evaluation kit and the results along with the source code for most trackers are publicly available at the challenge website 1 .

Chapter 16, “FOLDOUT: A Through Foliage Surveillance System for Border Security” was previously published non-open access. It has now been changed to open access under a CC BY 4.0 license and the copyright holder updated to ‘The Author(s)’. The book has also been updated with this change.

Improved methods for border surveillance are necessary to ensure an effective and efficient EU border management. In the border control context, as defined by the Schengen Border Code, border surveillance is defined as “the surveillance of borders between border crossing points and the surveillance of border crossing points outside the fixed opening hours, in order to prevent persons from circumventing border checks”. In this paper, an overview of an EU-funded program related to border security called FOLDOUT is described. FOLDOUT’s focus is on through foliage detection in the inner and outermost regions of the EU. The proposed FOLDOUT system will assist border guards by providing prompt detection of illegal activity at borders and trace the movement and routes prior to arrival in border areas. Different sensors will be developed to detect and locate people and vehicles operating under the coverage of trees and other foliage over large areas. In achieving this, a holistic system employing fused advanced ground, aerial and space-based sensors mounted on StratobusTM and satellite platforms is presented. The system will be demonstrated in different countries in order to test the performance under different weather conditions. Current results are related to FOLDOUT fusion demonstrator system which takes detections from three independent sensor types and unifies detections to eliminate inconsistencies and allow for a global view of activity in the border area.

The Visual Object Tracking challenge VOT2020 is the eighth annual tracker benchmarking activity organized by the VOT initiative. Results of 58 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The VOT2020 challenge was composed of five sub-challenges focusing on different tracking domains: (i) VOT-ST2020 challenge focused on short-term tracking in RGB, (ii) VOT-RT2020 challenge focused on “real-time” short-term tracking in RGB, (iii) VOT-LT2020 focused on long-term tracking namely coping with target disappearance and reappearance, (iv) VOT-RGBT2020 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2020 challenge focused on long-term tracking in RGB and depth imagery. Only the VOT-ST2020 datasets were refreshed. A significant novelty is introduction of a new VOT short-term tracking evaluation methodology, and introduction of segmentation ground truth in the VOT-ST2020 challenge – bounding boxes will no longer be used in the VOT-ST challenges. A new VOT Python toolkit that implements all these novelites was introduced. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net).

This paper addresses the problem of single-target tracker performance evaluation. We consider the performance measures, the dataset and the evaluation system to be the most important components of tracker evaluation and propose requirements for each of them. The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison. The ranking-based methodology addresses tracker equivalence in terms of statistical significance and practical differences. A fully-annotated dataset with per-frame annotations with several visual attributes is introduced. The diversity of its visual properties is maximized in a novel way by clustering a large number of videos according to their visual attributes. This makes it the most sophistically constructed and annotated dataset to date. A multi-platform evaluation system allowing easy integration of third-party trackers is presented as well. The proposed evaluation methodology was tested on the VOT2014 challenge on the new dataset and 38 trackers, making it the largest benchmark to date. Most of the tested trackers are indeed state-of-the-art since they outperform the standard baselines, resulting in a highly-challenging benchmark. An exhaustive analysis of the dataset from the perspective of tracking difficulty is carried out. To facilitate tracker comparison a new performance visualization technique is proposed., Comment: Final version (Accepted), IEEE Pattern Analysis and Machine Intelligence, 2016

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a “real-time” experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new long-term tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website (http://votchallenge.net). Funding agencies: Slovenian research agencySlovenian Research Agency - Slovenia [P2-0214, P2-0094, J2-8175]; Czech Science FoundationGrant Agency of the Czech Republic [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme 2017 Visua

The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website(1). Funding Agencies|Slovenian research agency research programs [P2-0214, P2-0094]; Slovenian research agency project [J2-8175]; Czech Science Foundation Project [GACR P103/12/G084]; WASP; VR (EMC2); SSF (SymbiCloud); SNIC; AIT Strategic Research Programme Visual Surveillance and Insight; Faculty of Computer Science, University of Ljubljana, Slovenia

Smart cameras perform on-board image analysis, adapt their algorithms to changes in their environment, and collaborate with other networked cameras to analyze the dynamic behavior of objects. A proposed computational framework adopts the concepts of self-awareness and self-expression to more efficiently manage the complex tradeoffs among performance, flexibility, resources, and reliability. The Web extra at http://youtu.be/NKe31_OKLz4 is a video demonstrating CamSim, a smart camera simulation tool, enables users to test self-adaptive and self-organizing smart-camera techniques without deploying a smart-camera network.

The use of reconfigurable computer vision architecture for image processing tasks is an important and challenging application in real time systems with limited resources. It is an emerging field as new computing architectures are developed, new algorithms are proposed and users define new emerging applications in surveillance. In this paper, a computer vision architecture capable of reconfiguring the processing chain of computer vision algorithms is summarised. The processing chain consists of multiple computer vision tasks, which can be distributed over various computing units. One key characteristic of the designed architecture is graceful degradation, which prevents the system from failure. This system characteristic is achieved by distributing computer vision tasks to other nodes and parametrizing each task depending on the specified quality-of-service. Experiments using an object detector applied to a public dataset are presented.

The Visual Object Tracking challenge VOT2016 aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 70 trackers are presented, with a large number of trackers being published at major computer vision conferences and journals in the recent years. The number of tested state-of-the-art trackers makes the VOT 2016 the largest and most challenging benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the Appendix. The VOT2016 goes beyond its predecessors by (i) introducing a new semi-automatic ground truth bounding box annotation methodology and (ii) extending the evaluation system with the no-reset experiment. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://votchallenge.net).

The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.

The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Link -- ping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.

We propose a novel method for establishing correspondences on deformable objects for single-target object tracking. The key ingredient is a dissimilarity measure between correspondences that takes into account their geometric compatibility, allowing us to separate inlier correspondences from outliers. We employ both static correspondences from the initial appearance of the object as well as adaptive correspondences from the previous frame to address the stability-plasticity dilemma. The geometric dissimilarity measure enables us to also disambiguate keypoints that are difficult to match. Based on these ideas we build a keypoint-based tracker that outputs rotated bounding boxes. We demonstrate in a rigorous empirical analysis that this tracker outperforms the state of the art on a dataset of 77 sequences.

We present a practical method for video surveillance networks to calibrate their cameras which have mostly non-overlapping field of views and might be tens of meters apart. The calibration or estimating the camera pose, focal length and radial distortion is an essential requirement in video surveillance systems for any further automated tasks like person tracking or flow monitoring. The proposed methodology casts the calibration as a localization problem of an image with respect to a 3D model which is built a priori with a moving camera. The method comprises state-of-the-art functioning blocks, the Structure from Motion (SfM) and minimal Perspective-n-Point (PnP) solvers, which were proved stable in 3D computer vision community and applies them in context of video surveillance. We demonstrate that the calibration method is effective in difficult repetitive, reflective and texture less large indoor environments like an airport.

The Visual Object Tracking challenge 2014, VOT2014, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 38 trackers are presented. The number of tested trackers makes VOT 2014 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2014 challenge that go beyond its VOT2013 predecessor are introduced: (i) a new VOT2014 dataset with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2013 evaluation methodology, (iii) a new unit for tracking speed assessment less dependent on the hardware and (iv) the VOT2014 evaluation toolkit that significantly speeds up execution of experiments. The dataset, the evaluation kit as well as the results are publicly available at the challenge website (http://​votchallenge.​net).

We propose a novel keypoint-based method for long-term model-free object tracking in a combined matching-and-tracking framework. In order to localise the object in every frame, each keypoint casts votes for the object center. As erroneous keypoints are hard to avoid, we employ a novel consensus-based scheme for outlier detection in the voting behaviour. To make this approach computationally feasible, we propose not to employ an accumulator space for votes, but rather to cluster votes directly in the image space. By transforming votes based on the current keypoint constellation, we account for changes of the object in scale and rotation. In contrast to competing approaches, we refrain from updating the appearance information, thus avoiding the danger of making errors. The use of fast keypoint detectors and binary descriptors allows for our implementation to run in real-time. We demonstrate experimentally on a diverse dataset that is as large as 60 sequences that our method outperforms the state-of-the-art when high accuracy is required and visualise these results by employing a variant of success plots.

Despite significant interest in the research community, the development of multi-camera applications is still quite challenging. This paper presents Ella - a dedicated publish/subscribe middleware system that facilitates distribution, component reuse and communication for heterogeneous multi-camera applications. We present the key components of this middleware system and demonstrate its applicability based on an autonomous multi-camera person tracking application. Ella is able to run on resource-limited and heterogeneous VSNs. We present performance measurements on different hardware platforms as well as operating systems.

In this work, we present an approach for improving the long-term association of keypoints in videos by exploiting the unlabeled parts of the video on-the-fly. Towards this end, we track keypoints from frame to frame by using a method for estimating optic flow. As long as this method is successful (according to certain criteria), we update an ensemble classifier with training data stemming from newly discovered views of the keypoint as well as from false matches. In each frame, we match candidate keypoints to the original keypoint by classifying them and re-initialise the tracking mechanism after failure. Neither a-priori knowledge about the keypoint nor a training stage is required. Our method avoids the use of an expensive sliding-window approach used by a similar method and instead embraces a highly efficient keypoint detection and matching stage, making our method suitable for the use in embedded devices. We show experimentally that our approach is able to provide both accurate and robust results on several sequences.

2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013; Sydney, NSW; Australia; 1 December 2013 through 8 December 2013, Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that would allow objective comparison of different tracking methods. To address this issue, the Visual Object Tracking (VOT) workshop was organized in conjunction with ICCV2013. Researchers from academia as well as industry were invited to participate in the first VOT2013 challenge which aimed at single-object visual trackers that do not apply pre-learned models of object appearance (model-free). Presented here is the VOT2013 benchmark dataset for evaluation of single-object visual trackers as well as the results obtained by the trackers competing in the challenge. In contrast to related attempts in tracker benchmarking, the dataset is labeled per-frame by visual attributes that indicate occlusion, illumination change, motion change, size change and camera motion, offering a more systematic comparison of the trackers. Furthermore, we have designed an automated system for performing and evaluating the experiments. We present the evaluation protocol of the VOT2013 challenge and the results of a comparison of 27 trackers on the benchmark dataset. The dataset, the evaluation tools and the tracker rankings are publicly available from the challenge website (http://votchallenge. net).

We present a novel analysis of the state of the art in object tracking with respect to diversity found in its main component, an ensemble classifier that is updated in an online manner. We employ established measures for diversity and performance from the rich literature on ensemble classification and online learning, and present a detailed evaluation of diversity and performance on benchmark sequences in order to gain an insight into how the tracking performance can be improved.

Deployment of existing vision approaches in camera networks for applications such as human tracking show a large gap between user expectation and current results. Calibrated cameras could push these approaches closer to applicability, as physical constraints greatly complement the ill-posed acquisition process. Calibrated cameras promise also new applications as spatial relationships among cameras and the environment capture additional information. However, a convenient calibration is still a challenge on its own. This paper presents a novel calibration framework for large networks including non-overlapping cameras. The framework purely relies on visual information coming from walking people. Since non-overlapping scenarios make point correspondences impossible, time constancy of a person’s motion introduces the missing complementary information. The framework obtains calibrated cameras starting from single camera calibration thereby bringing the problem to a reduced form suitable for multi-view calibration. It extends the standard bundle adjustment by a smoothness constraint to avoid the ill-posed problem arising from missing point correspondences. The stratified optimization suppresses the danger to get stuck in local minima. Experiments with synthetic and real data validate the approach.

The paper presents a novel approach for single object tracking across non-overlapping camera views, which searches for the optimal association of single view trajectories. We map the tracking problem to a tree structure and introduce a branch and bound approach to efficiently explore the search space. We use an optimization criterion based solely on the geometric cues coming from the calibration of the network. The cost function is defined as to enforce consistency of geometrical and kinematic properties over the whole trajectory path. We show how the information content of the geometric properties of the network brings a substantial contribution to solve the association problem. Experiments in a set-up of four cameras using both synthetic and real trajectories validate the advantages of the approach both in terms of performance and information content of the geometric cue.

Welcome to the First Industrial Surveillance Day (ISD 2011) organized by AIT Austrian Institute of Technology and hosted by the Eight IEEE International Conference on Advanced Video and Signal- Based Surveillance (AVSS 2011).

This paper presents a novel neuromorphic camera system rotating at high-speed (1 to 4 rotations/sec) to acquire 360° panoramas in real-time by exploiting the high temporal resolution, the high dynamic range and the sparse visual information representation using a neuromorphic vision sensor with address-event (AE) signaling mounted on a high-speed mechanical rotation device. Contrary to state-of-the-art panorama cameras (e.g. rotational cameras or catadioptric cameras), this camera system can delivers several distortion-free 360° panoramas per second at constant image resolution and efficient edge extraction of the scene under real illumination conditions without any further computation. This camera system could establish new sensing capabilities in challenging applications such as real-time environmental awareness for robotics and surveillance. After introducing panorama systems and the neuromorphic dual-line dynamic vision sensor, the new camera concept is presented. A comparative analysis of this system with state-of-the art cameras is given. The concept, the camera design and resulting images using an existing 256 pixel line sensor are presented.

This paper presents a new approach to the problem of camera localization with non-overlapping camera views, particularly relevant for video surveillance systems. We show how to recast localization as quasi-convex optimization under the L-Infinity norm. Thereby we add the problem of reconstructing camera centers and 3D points for non-overlapping cameras with known internal parameters and known rotations to the class of known geometric problems solvable with Second Order Cone Programming. The 3D points are never seen by more than one camera, which makes the localization problem ill-posed. Therefore, the proposed approach employs temporal consistency of the 3D points to supply the missing constraints. Our formulation allows a global optimal solution to be found with a clear physical meaning of the cost function being minimized.

This paper proposes a method that localizes two surveillance cameras and simultaneously reconstructs object trajectories in 3D space. The method is an extension of the Direct Reference Plane method, which formulates the localization and the reconstruction as a system of linear equations that is globally solvable by Singular Value Decomposition. The method's assumptions are static synchronized cameras, smooth trajectories, known camera internal parameters, and the rotation between the cameras in a world coordinate system. The paper describes the method in the context of self-calibrating cameras, where the internal parameters and the rotation can be jointly obtained assuming a man-made scene with orthogonal structures. Experiments with synthetic and real--image data show that the method can recover the camera centers with an error less than half a meter even in the presence of a 4 meter gap between the fields of view.

This paper presents a stratified auto-calibration framework for typical large surveillance set-ups including non-overlapping cameras. The framework avoids the need of any calibration target and purely relies on visual information coming from walking people. Since in non-overlapping scenarios there are no point correspondences across the cameras the standard techniques cannot be employed. We show how to obtain a fully calibrated camera network starting from single camera calibration and bringing the problem to a reduced form suitable for multiview calibration. We extend the standard bundle adjustment by a smoothness constraint to avoid the ill-posed problem arising from missing point correspondences. The proposed framework optimizes the objective function in a stratified manner thus suppressing the problem of local minima. Experiments with synthetic and real data validate the approach.

The automatic calibration of the intrinsic camera parameters such as the focal length and the camera orientation is an important pre-requisite for many computer vision algorithms in video surveillance. Despite its importance only a few number of methods show their applicability in embedded systems. This paper shows new results of previous work done in camera self-calibration on images of the York Urban data-set. These 102 images show typical urban scenes one might expect in practice. The evaluation shows that in 52 of 102 images the proposed method achieves less than 5% relative error in the focal length at a mean computation time per image of 14.45 s on a standard PC. We believe that these results show a fair balance between accuracy and computational performance and encourage an embedded implementation on a smart camera.

This chapter introduces intrinsic camera calibration with focus on video surveillance. Calibration of camera-specific parameters such as the focal length is mandatory for metrological problems, for example, measuring a vehicle’s speed. However, it also improves target classification, target detection, and target tracking. Geometry has become important in multi-camera systems that hand over and track objects across cameras. We present the basic geometric concept behind calibration and show which information about the cameras, the scene, and the images is necessary to realize automatic methods. Self-calibration will be a key technology for the practical deployment of future smart video cameras.

Tracking across non-overlapping camera views is still an unsolved problem. Appearance is a popular cue that does not work when the views are considerably different. This paper proposes tracking in the 3-D space without using appearance. We propose to use the geometry between the two cameras. Linear inhomogeneous triangulation is expanded by a Gaussian random walk model. Tracking is then triangulation followed by a re-projection given the assumption that two persons cannot occupy the same space at the same time. A first experiment with a single person shows the success of this new tracking approach even with a 2m gap between the fields of view of the cameras.

People tracking is of fundamental importance in multi-camera surveillance systems. In recent years, many approaches for multi-camera tracking have been discussed. Most methods use either various image features or the geometric relation between the cameras or both as a cue. It is a desire to know the geometry for distant cameras, because geometry is not influenced by, for example, drastic changes in object appearance or in scene illumination. However, the determination of the camera geometry is cumbersome. The paper tries to solve this problem and contributes in two different ways. On the one hand, an approach is presented that calibrates two distant cameras automatically. We continue previous work and focus especially on the calibration of the extrinsic parameters. Point correspondences are used for this task which are acquired by detecting points on top of people's heads. On the other hand, qualitative experimental results with the PETS 2006 benchmark data show that the self-calibration is accurate enough for a solely geometric tracking of people across distant cameras. Reliable features for a matching are hardly available in such cases.

This paper proposes a method to compute the fundamental matrix from slightly overlapping views. Slight overlaps are preferable to substantial overlaps in large multi-camera surveillance applications, because otherwise the number of cameras and thus the overall application costs would gravely increase. Instead of using point correspondences alone, we additionally use the infinite homography. The infinite homography can be computed from line segments when we have a Manhattan world scene and assume cameras with square pixels and the same alignment of the scene in both views. All assumptions are not too restrictive and occur frequently. The infinite homography and one of the epipoles determine the fundamental matrix. The paper demonstrates a simple solution to compute the epipoles from at least two point correspondences without requiring points uniformly distributed within the images. Experiments on synthetic images clearly show the advantages of the proposed method compared to the classical eight point algorithm. Images of an office scene show the idea on real world images.

This paper proposes a method to auto-calibrate a static camera in man-made worlds. The calibration is done with vanishing points which are estimated with line segments. Instead of a single image, the method uses constantly acquired images from a camera. This online behavior of the method has two advantages: A continuous and adaptive estimation of the vanishing points over thousands of images suppresses the amount of noise in their positions. Newly detected line segments can further improve the estimated positions of vanishing points. This method is of practical importance, because simple installation and maintenance of cameras in video surveillance is a competitive advantage. Furthermore, applications like tracking can profit, because knowledge about the scene geometry can be a potential clue. The method was evaluated in a broad range of indoor and outdoor environments. The relative error between the internal camera parameter estimates and the true values was on average 5%.

CCTV and image analysis systems for automatic incident detection are major tools in tunnel safety management. This paper presents results of a feasibility study called VITUS-1. Among others, one aim of VITUS-I was to compare image analysis methods for robust object detection, tracking and incident detection. Image quality improvements were shown by using digital camera technology instead of state-of-the-art analogue CCTV. To improve image quality and robustness of existing image analysis methods, requirements on several camera properties were defined. We want to sensitise primarily tunnel operators and tunnel CCTV experts that commercial image analysis systems usually do not claim such requirements, e.g. requirements on resolution. This paper shows with a simple camera model that a robust detection of test objects is hardly feasible with typical tunnel CCTV (PAL resolution, 20/spl deg/ viewing angle, 212m camera distance). As a consequence, the specifications of tunnel CCTV should be adjusted to the needs of image analysis systems. Simple experiments with real video data show the applicability of the camera model.