The Thermal Infrared Visual Object Tracking VOT-TIR2015 challenge results

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

Robust object tracking based on tracking-learning-detection

Zsfassung in dt. SpracheAktuelle Objektverfolgungsmethoden am Stand der Technik verwenden adaptives Tracking-By-Detection, was bedeutet, dass ein Detektor die Position eines Objekts ermittelt und gleichzeitig seine Parameter an die Erscheinung des Objekts anpasst. Während solche Methoden in Fällen funktionieren, in denen das Objekt nicht vom Schauplatz verschwindet, neigen sie dazu, bei Verdeckungen fehlzuschlagen. In dieser Arbeit bauen wir auf einem neuen Ansatz auf, der Tracking-Learning-Detection (TLD) genannt wird und der dieses Problem bewältigt.In TLD-Methoden wird der Detektor mit Beispielen trainiert, die auf der Trajektorie eines Trackers liegen, der unabhängig vom Detektor ist.Durch die Entkopplung von Objektverfolgung und Objektdetektion erreichen wir eine große Robustheit und übertreffen existierende adaptive Tracking-By-Detection-Methoden. Wir zeigen, dass durch den Einsatz von einfachen Features zur Objekterkennung und mit der Verwendung eines kaskadierten Ansatzes eine beträchtliche Reduktion der Rechenzeit erzielt wird. Wir evaluieren unseren Ansatz sowohl auf existierenden Standarddatensätzen in einer Kamera als auch auf neu aufgenommenen Sequenzen in mehreren Kameras.Current state-of-the-art methods for object tracking perform adaptive tracking-by-detection, meaning that a detector predicts the position of an object and adapts its parameters to the object's appearance at the same time.While suitable for cases when the object does not disappear from the scene, these methods tend to fail on occlusions. In this work, we build on a novel approach called Tracking-Learning-Detection (TLD) that overcomes this problem.In methods based on TLD, a detector is trained with examples found on the trajectory of a tracker that itself does not depend on the object detector.By decoupling object tracking and object detection we achieve high robustness and outperform existing adaptive tracking-by-detection methods. We show that by using simple features for object detection and by employing a cascaded approach a considerable reduction of computing time is achieved. We evaluate our approach both on existing standard single-camera datasets as well as on newly recorded sequences in multi-camera scenarios.5

Ella: Middleware for Multi-camera Surveillance in Heterogeneous Visual Sensor Networks

Abstract—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 multicamera 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. I

The visual object tracking VOT2013 challenge results

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)

The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results

The Thermal Infrared Visual Object Tracking challenge 2015, VOTTIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply prelearned 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 (Linköping 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

Учебная программа по учебной дисциплине "Химия"

Учебная программа " Химия" кафедры "Материаловедение в машиностроении" для дневной формы получения образования: общее количество часов – 184, трудоемкость учебной дисциплины – 5 з.е., форма контроля знаний – экзамен

The Visual Object Tracking VOT2015 challenge results

The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website(1)