Real-time RGB-D camera pose estimation in novel scenes using a relocalisation cascade

Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by adapting a pre-trained forest to a new scene on the fly. The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time. In this paper, we present an extension of this work that achieves significantly better relocalisation performance whilst running fully in real time. To achieve this, we make several changes to the original approach: (i) instead of accepting the camera pose hypothesis without question, we make it possible to score the final few hypotheses using a geometric approach and select the most promising; (ii) we chain several instantiations of our relocaliser together in a cascade, allowing us to try faster but less accurate relocalisation first, only falling back to slower, more accurate relocalisation as necessary; and (iii) we tune the parameters of our cascade to achieve effective overall performance. These changes allow us to significantly improve upon the performance our original state-of-the-art method was able to achieve on the well-known 7-Scenes and Stanford 4 Scenes benchmarks. As additional contributions, we present a way of visualising the internal behaviour of our forests and show how to entirely circumvent the need to pre-train a forest on a generic scene

Struck: Structured Output Tracking with Kernels.

Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use online learning techniques to update the object model. However, for these updates to happen one needs to convert the estimated object position into a set of labelled training examples, and it is not clear how best to perform this intermediate step. Furthermore, the objective for the classifier (label prediction) is not explicitly coupled to the objective for the tracker (estimation of object position). In this paper, we present a framework for adaptive visual object tracking based on structured output prediction. By explicitly allowing the output space to express the needs of the tracker, we avoid the need for an intermediate classification step. Our method uses a kernelised structured output support vector machine (SVM), which is learned online to provide adaptive tracking. To allow our tracker to run at high frame rates, we (a) introduce a budgeting mechanism that prevents the unbounded growth in the number of support vectors that would otherwise occur during tracking, and (b) show how to implement tracking on the GPU. Experimentally, we show that our algorithm is able to outperform state-of-the-art trackers on various benchmark videos. Additionally, we show that we can easily incorporate additional features and kernels into our framework, which results in increased tracking performance

The sixth visual object tracking VOT2018 challenge results

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)

The visual object tracking VOT2017 challenge results

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 website1

Acute phase response in two consecutive experimentally induced E. coli intramammary infections in dairy cows

<p>Abstract</p> <p>Background</p> <p>Acute phase proteins haptoglobin (Hp), serum amyloid A (SAA) and lipopolysaccharide binding protein (LBP) have suggested to be suitable inflammatory markers for bovine mastitis. The aim of the study was to investigate acute phase markers along with clinical parameters in two consecutive intramammary challenges with <it>Escherichia coli </it>and to evaluate the possible carry-over effect when same animals are used in an experimental model.</p> <p>Methods</p> <p>Mastitis was induced with a dose of 1500 cfu of <it>E. coli </it>in one quarter of six cows and inoculation repeated in another quarter after an interval of 14 days. Concentrations of acute phase proteins haptoglobin (Hp), serum amyloid A (SAA) and lipopolysaccharide binding protein (LBP) were determined in serum and milk.</p> <p>Results</p> <p>In both challenges all cows became infected and developed clinical mastitis within 12 hours of inoculation. Clinical disease and acute phase response was generally milder in the second challenge. Concentrations of SAA in milk started to increase 12 hours after inoculation and peaked at 60 hours after the first challenge and at 44 hours after the second challenge. Concentrations of SAA in serum increased more slowly and peaked at the same times as in milk; concentrations in serum were about one third of those in milk. Hp started to increase in milk similarly and peaked at 36–44 hours. In serum, the concentration of Hp peaked at 60–68 hours and was twice as high as in milk. LBP concentrations in milk and serum started to increase after 12 hours and peaked at 36 hours, being higher in milk. The concentrations of acute phase proteins in serum and milk in the <it>E. coli </it>infection model were much higher than those recorded in experiments using Gram-positive pathogens, indicating the severe inflammation induced by <it>E. coli</it>.</p> <p>Conclusion</p> <p>Acute phase proteins would be useful parameters as mastitis indicators and to assess the severity of mastitis. If repeated experimental intramammary induction of the same animals with <it>E. coli </it>is used in cross-over studies, the interval between challenges should be longer than 2 weeks, due to the carry-over effect from the first infection.</p