Point Pair Feature based Object Detection for Random Bin Picking

Point pair features are a popular representation for free form 3D object detection and pose estimation. In this paper, their performance in an industrial random bin picking context is investigated. A new method to generate representative synthetic datasets is proposed. This allows to investigate the influence of a high degree of clutter and the presence of self similar features, which are typical to our application. We provide an overview of solutions proposed in literature and discuss their strengths and weaknesses. A simple heuristic method to drastically reduce the computational complexity is introduced, which results in improved robustness, speed and accuracy compared to the naive approach

The Dialectic of the Theology of Browning\u27s Bishop Blougram

Detecting and tracking people in images is an attractive method to monitor their movements. It is based on passive, non-contact sensors and hence does not disturb or distract the subjects. The analysis of the extracted position and pose data can be used in applications such as security and safety monitoring, home automation, patient monitoring or behavior analysis. However, detecting people in images is a challenging problem. Differences in pose, clothing and lighting (along with other factors) cause a lot of variation in their appearance. Some solutions have been proposed but they typically have mediocre accuracy, suffer from severe limitations, require large amounts of annotated training data and are computationally expensive. To overcome these issues, we propose a system based on fused range and thermal infrared images. These measurements show considerably less variation and provide more meaningful information. We provide a brief introduction to the sensor technology used and propose a calibration method. Several data fusion algorithms are compared and their performance is assessed on a simulated data set. The results of initial experiments are shown and the measurement errors and the challenges they present are discussed. The resulting fused data are used to efficiently detect people in a fixed camera setup. The system can be extended to include person tracking.status: publishe

How low can you go? Privacy-preserving people detection with an omni-directional camera

In this work, we use a ceiling-mounted omni-directional camera to detect people in a room. This can be used as a sensor to measure the occupancy of meeting rooms and count the amount of flex-desk working spaces available. If these devices can be integrated in an embedded low-power sensor, it would form an ideal extension of automated room reservation systems in office environments. The main challenge we target here is ensuring the privacy of the people filmed. The approach we propose is going to extremely low image resolutions, such that it is impossible to recognise people or read potentially confidential documents. Therefore, we retrained a single-shot low-resolution person detection network with automatically generated ground truth. In this paper, we prove the functionality of this approach and explore how low we can go in resolution, to determine the optimal trade-off between recognition accuracy and privacy preservation. Because of the low resolution, the result is a lightweight network that can potentially be deployed on embedded hardware. Such embedded implementation enables the development of a decentralised smart camera which only outputs the required meta-data (i.e. the number of persons in the meeting room)

Embedded Line Scan Image Sensors: The Low Cost Alternative for High Speed Imaging

In this paper we propose a low-cost high-speed imaging line scan system. We replace an expensive industrial line scan camera and illumination with a custom-built set-up of cheap off-the-shelf components, yielding a measurement system with comparative quality while costing about 20 times less. We use a low-cost linear (1D) image sensor, cheap optics including a LED-based or LASER-based lighting and an embedded platform to process the images. A step-by-step method to design such a custom high speed imaging system and select proper components is proposed. Simulations allowing to predict the final image quality to be obtained by the set-up has been developed. Finally, we applied our method in a lab, closely representing the real-life cases. Our results shows that our simulations are very accurate and that our low-cost line scan set-up acquired image quality compared to the high-end commercial vision system, for a fraction of the price.Comment: 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA

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

Semi-automatic annotation of eye-tracking recordings in terms of human torso, face and hands

status: publishe