Calibration of Asynchronous Camera Networks: CALICO

Camera network and multi-camera calibration for external parameters is a necessary step for a variety of contexts in computer vision and robotics, ranging from three-dimensional reconstruction to human activity tracking. This paper describes CALICO, a method for camera network and/or multi-camera calibration suitable for challenging contexts: the cameras may not share a common field of view and the network may be asynchronous. The calibration object required is one or more rigidly attached planar calibration patterns, which are distinguishable from one another, such as aruco or charuco patterns. We formulate the camera network and/or multi-camera calibration problem using rigidity constraints, represented as a system of equations, and an approximate solution is found through a two-step process. Simulated and real experiments, including an asynchronous camera network, multicamera system, and rotating imaging system, demonstrate the method in a variety of settings. Median reconstruction accuracy error was less than $0.41$ mm$^2$ for all datasets. This method is suitable for novice users to calibrate a camera network, and the modularity of the calibration object also allows for disassembly, shipping, and the use of this method in a variety of large and small spaces.Comment: 11 page

Tracking human movement in office environment using video processing

In this paper, we proposed an approach of multi-person movement tracking in office environment without any identity conflicts. Simple image processing with frame differentiation method is applied to identify multiple human motion. An Expert System is applied to predict next camera occurrence of the tracking human. The main objective of this work is to detect and track multi-human motion using single camera in more than a room in an office

Exploring Computation-Communication Tradeoffs in Camera Systems

Cameras are the defacto sensor. The growing demand for real-time and low-power computer vision, coupled with trends towards high-efficiency heterogeneous systems, has given rise to a wide range of image processing acceleration techniques at the camera node and in the cloud. In this paper, we characterize two novel camera systems that use acceleration techniques to push the extremes of energy and performance scaling, and explore the computation-communication tradeoffs in their design. The first case study targets a camera system designed to detect and authenticate individual faces, running solely on energy harvested from RFID readers. We design a multi-accelerator SoC design operating in the sub-mW range, and evaluate it with real-world workloads to show performance and energy efficiency improvements over a general purpose microprocessor. The second camera system supports a 16-camera rig processing over 32 Gb/s of data to produce real-time 3D-360 degree virtual reality video. We design a multi-FPGA processing pipeline that outperforms CPU and GPU configurations by up to 10x in computation time, producing panoramic stereo video directly from the camera rig at 30 frames per second. We find that an early data reduction step, either before complex processing or offloading, is the most critical optimization for in-camera systems

FieldSAFE: Dataset for Obstacle Detection in Agriculture

In this paper, we present a novel multi-modal dataset for obstacle detection in agriculture. The dataset comprises approximately 2 hours of raw sensor data from a tractor-mounted sensor system in a grass mowing scenario in Denmark, October 2016. Sensing modalities include stereo camera, thermal camera, web camera, 360-degree camera, lidar, and radar, while precise localization is available from fused IMU and GNSS. Both static and moving obstacles are present including humans, mannequin dolls, rocks, barrels, buildings, vehicles, and vegetation. All obstacles have ground truth object labels and geographic coordinates.Comment: Submitted to special issue of MDPI Sensors: Sensors in Agricultur

Efficient 2D-3D Matching for Multi-Camera Visual Localization

Visual localization, i.e., determining the position and orientation of a vehicle with respect to a map, is a key problem in autonomous driving. We present a multicamera visual inertial localization algorithm for large scale environments. To efficiently and effectively match features against a pre-built global 3D map, we propose a prioritized feature matching scheme for multi-camera systems. In contrast to existing works, designed for monocular cameras, we (1) tailor the prioritization function to the multi-camera setup and (2) run feature matching and pose estimation in parallel. This significantly accelerates the matching and pose estimation stages and allows us to dynamically adapt the matching efforts based on the surrounding environment. In addition, we show how pose priors can be integrated into the localization system to increase efficiency and robustness. Finally, we extend our algorithm by fusing the absolute pose estimates with motion estimates from a multi-camera visual inertial odometry pipeline (VIO). This results in a system that provides reliable and drift-less pose estimation. Extensive experiments show that our localization runs fast and robust under varying conditions, and that our extended algorithm enables reliable real-time pose estimation.Comment: 7 pages, 5 figure

Multi-camera complexity assessment system for assembly line work stations

In the last couple of years, the market demands an increasing number of product variants. This leads to an inevitable rise of the complexity in manufacturing systems. A model to quantify the complexity in a workstation has been developed, but part of the analysis is done manually. Thereto, this paper presents the results of an industrial proof-of-concept in which the possibility of automating the complexity analysis using multi camera video images, was tested

3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection

Cameras are a crucial exteroceptive sensor for self-driving cars as they are low-cost and small, provide appearance information about the environment, and work in various weather conditions. They can be used for multiple purposes such as visual navigation and obstacle detection. We can use a surround multi-camera system to cover the full 360-degree field-of-view around the car. In this way, we avoid blind spots which can otherwise lead to accidents. To minimize the number of cameras needed for surround perception, we utilize fisheye cameras. Consequently, standard vision pipelines for 3D mapping, visual localization, obstacle detection, etc. need to be adapted to take full advantage of the availability of multiple cameras rather than treat each camera individually. In addition, processing of fisheye images has to be supported. In this paper, we describe the camera calibration and subsequent processing pipeline for multi-fisheye-camera systems developed as part of the V-Charge project. This project seeks to enable automated valet parking for self-driving cars. Our pipeline is able to precisely calibrate multi-camera systems, build sparse 3D maps for visual navigation, visually localize the car with respect to these maps, generate accurate dense maps, as well as detect obstacles based on real-time depth map extraction

Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks

This paper proposes a novel system to estimate and track the 3D poses of multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D pose of each person is computed by a central node which receives the single-view outcomes from each camera of the network. Each single-view outcome is computed by using a CNN for 2D pose estimation and extending the resulting skeletons to 3D by means of the sensor depth. The proposed system is marker-less, multi-person, independent of background and does not make any assumption on people appearance and initial pose. The system provides real-time outcomes, thus being perfectly suited for applications requiring user interaction. Experimental results show the effectiveness of this work with respect to a baseline multi-view approach in different scenarios. To foster research and applications based on this work, we released the source code in OpenPTrack, an open source project for RGB-D people tracking.Comment: Submitted to the 2018 IEEE International Conference on Robotics and Automatio