The autonomous hidden camera crew

Reality TV shows that follow people in their day-to-day lives are not a new concept. However, the traditional methods used in the industry require a lot of manual labour and need the presence of at least one physical camera man. Because of this, the subjects tend to behave differently when they are aware of being recorded. This paper will present an approach to follow people in their day-to-day lives, for long periods of time (months to years), while being as unobtrusive as possible. To do this, we use unmanned cinematographically-aware cameras hidden in people's houses. Our contribution in this paper is twofold: First, we create a system to limit the amount of recorded data by intelligently controlling a video switch matrix, in combination with a multi-channel recorder. Second, we create a virtual camera man by controlling a PTZ camera to automatically make cinematographically pleasing shots. Throughout this paper, we worked closely with a real camera crew. This enabled us to compare the results of our system to the work of trained professionals.Comment: 4 pages, 6 figure

An OpenCL implementation of a forward sampling algorithm for CP-logic

We present an approximate query answering algorithm for the Probabilistic Logic Programming language CP-logic. It complements existing sampling algorithms by using the rules from body to head in- stead of in the other direction. We present an implementation in OpenCL, which is able to exploit the multicore architecture of modern GPUs to compute a large number of samples in parallel, and demonstrate that this is competitive with existing inference algorithms.status: publishe

An OpenCL implementation of a forward sampling algorithm for CP-logic

We present an approximate query answering algorithm for the Probabilistic Logic Programming language CP-logic. It complements existing sampling algorithms by using the rules from body to head instead of in the other direction. We present an implementation in OpenCL, which is able to exploit the multicore architecture of modern GPUs to compute a large number of samples in parallel, and demonstrate that this is competitive with existing inference algorithms.publisher: Elsevier articletitle: An OpenCL implementation of a forward sampling algorithm for CP-logic journaltitle: International Journal of Approximate Reasoning articlelink: http://dx.doi.org/10.1016/j.ijar.2015.05.008 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved.status: publishe

Detection of photovoltaic installations in RGB aerial imaging: a comparative study

In this work, we compare four different approaches for detecting photovoltaic installations from RGB aerial images. Our client, an electricity grid administrator, wants to hunt down fraud with unregistered illegal solar panel installations by detecting installations in aerial imagery and checking these against their database of registered installations. The detection of solar panels in these RGB images is a difficult task. Reasons are the relatively low resolution (at 25 cm/pixel an individual solar panel only measures about 9 × 7 pixels), the undiscriminating colour properties of the object (due to in-class variance and specular effects) and the apparent shape variability (rotation and skew due to the different roofs slant angles). Therefore, straightforward object segmentation techniques do not yield a satisfying solution, as proven in this paper. We compared four state-of-the-art object detection approaches for this task. First we experimented with a machine learning object detection technique based on pixel-based support vector machine classification. Secondly we developed an approach using MSER based colour segmentation and shape analysis. Finally a dual approach based on object categorization using the boosted cascade classifier technique of Viola & Jones and the aggregate channel features technique of Doll ́ar et al., is introduced, learning a combination of colour and gradient feature based classifiers from a given training set. We successfully evaluate these four different approaches on a fully labelled test set of a 8000 × 8000 pixel, 4 square km zone containing 315 solar panel installations with in total more than 10.000 individual panels.status: publishe

Ultra-low-latency automatic endoscopic image orientation stabilisation

Minimally invasive surgery often makes use of endoscopes with an angled lens. When using such an endoscope, the on- screen image tends to rotate wildly, which may confuse the surgeon. A low-impact way of solving this problem is by using computer vision techniques to track the rotation of the incoming footage and then counterrotating the image. Such an approach has already been proposed in the literature, but it has never been examined whether it is possible to integrate such an algorithm into a state-of-the-art Digital Operating Room environment, where minimal latency is required. In this paper, we compare three different ways of implementing a counterrotation algorithm on the hardware that is available in the NUCLeUS Digital Operating Room of the company eSaturnus.status: publishe

Automatic Camera Selection and PTZ Canvas Steering for Autonomous Filming of Reality TV

Reality TV shows that follow people in their day-to-day lives are not a new concept. However, the traditional methods used in the industry require a lot of manual labor and need the presence of at least one physical camera man. Because of this, the subjects tend to behave differently when they are aware of being recorded. This paper presents an approach to follow people in their day-to-day lives, for long periods of time (months to years), while being as unobtrusive as possible. To do this, we use unmanned cinematographically-aware cameras hidden in people's houses. Our contribution in this paper is twofold: First, we create a system to limit the amount of recorded data by intelligently controlling a video switch matrix, in combination with a multi-channel recorder. Second, we create a virtual camera man by controlling a PTZ camera to automatically make cinematographically pleasing shots. Throughout this paper, we worked closely with a real camera crew, enabling us to compare the results of our system to the work of trained professionals. This work was originally published in MVA 2017, as T. Callemein, W. Van Ranst and T. Goedemé, "The Autonomous hidden Camera Crew".* T. Callemein and W. Van Ranst contributed equally to this paper.status: publishe

Fooling automated surveillance cameras: adversarial patches to attack person detection

Adversarial attacks on machine learning models have seen increasing interest in the past years. By making only subtle changes to the input of a convolutional neural network, the output of the network can be swayed to output a completely different result. The first attacks did this by changing pixel values of an input image slightly to fool a classifier to output the wrong class. Other approaches have tried to learn ``patches'' that can be applied to an object to fool detectors and classifiers. Some of these approaches have also shown that these attacks are feasible in the real-world, i.e. by modifying an object and filming it with a video camera. However, all of these approaches target classes that contain almost no intra-class variety (e.g. stop signs). The known structure of the object is then used to generate an adversarial patch on top of it. In this paper, we present an approach to generate adversarial patches to targets with lots of intra-class variety, namely persons. The goal is to generate a patch that is able successfully hide a person from a person detector. An attack that could for instance be used maliciously to circumvent surveillance systems, intruders can sneak around undetected by holding a small cardboard plate in front of their body aimed towards the surveilance camera. From our results we can see that our system is able significantly lower the accuracy of a person detector. Our approach also functions well in real-life scenarios where the patch is filmed by a camera. To the best of our knowledge we are the first to attempt this kind of attack on targets with a high level of intra-class variety like persons.status: publishe

Automatic Endoscopic Image Orientation Stabilisation with Ultra-Low-Latency

Minimally invasive surgery often makes use of endoscopes with an angled lens. When using such an endoscope, the on-screen image tends to rotate wildly, which may confuse the surgeon. A low-impact way of solving this problem is by using computer vision techniques to track the rotation of the incoming footage and then counterrotating the image. Such an approach has already been proposed in the literature, but it has never been examined whether it is possible to integrate such an algorithm into a state-of-the-art Digital Operating Room environment, where minimal latency is required. In this paper, we compare three different ways of implementing a counterrotation algorithm on the hardware that is available in the NUCLeUS Digital Operating Room of the company eSaturnus. KEYWORDS Endoscopy, Image stabilisation, Ultra-low-latencystatus: publishe

GPU Accelerated ACF Detector

The field of pedestrian detection has come a long way in recent decades. In terms of accuracy, the current state-of-the-art is hands down reached by Deep Learning methods. However in terms of running speed this is not always the case, traditional methods are often still faster than their Deep Learning counterparts. This is especially true on embedded hardware, embedded platforms are often used in applications that require realtime performance while at same the time having to make do with a limited amount of resources. In this paper we present a GPU implementation of the ACF pedestrian detector and compare it to current Deep Learning approaches (YOLO) on both a desktop GPU as well as the Jetson TX2 embedded GPU platform.status: publishe