4 research outputs found
An automatic calibration method for stereo-based 3D distributed smart camera networks
Stereo-based 3D distributed smart camera networks are useful in a broad range of applications. Knowledge of the relative locations and orientations of nodes in the network is an essential prerequisite for true 3D sensing. A novel spatial calibration method for a network of pre-calibrated stereo smart cameras is presented, which obtains pose estimates suitable for collaborative 3D vision in a distributed fashion using two stages of registration on robust 3D point sets. The method is initially described in a geometrical sense, then presented in a practical implementation using existing vision and registration algorithms. Experiments using both software simulations and physical devices are designed and executed to demonstrate performance
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Multistep Explicit Stereo Camera Calibration Approach to Improve Euclidean Accuracy of Large-Scale 3D Reconstruction
The spatial accuracy of point clouds generated by stereo image-based 3D reconstruction algorithms is very sensitive to the intrinsic and extrinsic camera parameters determined during camera calibration. The existing camera calibration algorithms induce a significant amount of error due to poor estimation accuracies in camera parameters when they are used for large-scale scenes such as mapping civil infrastructure. This leads to higher uncertainties in the location of 3D points, and may result in the failure of the whole reconstruction process. This paper proposes a novel procedure to address this problem. It hypothesizes that a set of multiple calibrations created by videotaping a moving calibration pattern along a specific path can increase overall calibration accuracy. This is achieved by using conventional camera calibration algorithms to perform separate estimations for some predefined distance values. The result, which includes multiple sets of camera parameters, is then used in the Structure from Motion process to improve the Euclidean accuracy of the reconstruction. The proposed method has been tested on infrastructure scenes and experimental analyses indicate more than 25% improvement in the spatial accuracy of 3D points.This is the accepted manuscript. The final version is available from ASCE at http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.000045
Establishing a Quantifiable Model of Whale Shark Avoidance Behaviours to Anthropogenic Impacts in Tourism Encounters to Inform Management Actions
As the world's largest living fish, the whale shark has received much scientific attention in recent years, although despite this a great deal is still unknown on the life history and behavioural ecology of these majestic sharks. Whale shark related tourism has exploded in the last two decades from only a few sites in the 1990s to more than 12 sites internationally, allowing it to become a highly lucrative industry based upon this Vulnerable species. This study assesses the effects of anthropogenic impact on the sharksâ avoidance behaviours within modern day tourism encounters, and provides recommendations on how to control and reduce unnecessary disturbance to the species. By means of stereo-photogrammetry, continuous high definition videos of human-animal interactions were recorded and analyzed for behavioural changes against pre-selected independant variables. The use of Stereo-photogrammetry imagery also allowed for the accumulation of repeatable, proximity measurements of swimmer distance to the shark, permitting more precise and accurate results. Avoidance behaviours of 33 individual whale sharks were monitored during typical tourism encounters (n=75). A total of 192 search hours were documented over the collection periods, which incorporated three-aggregation sites spanning the Indian Ocean (the Seychelles, the Philipines & Mozambique). A generalized linear model demonstrated that proximity of swimmers to the shark was found to be significant (p=0.0295) in explaining the probability of the whale sharks showing disturbed behaviour. A proportional odds plot for proximity was developed to give an indication of the animals disturbance level in tourism interactions. At recommended distances of three metres from the sides of the shark, there is on average a 42% chance of disturbance, while at the distance of four metres from the tail area results showed a 31% chance of overall disturbance. The true estimate for either distance is likely to lie between 22-53% respectively with regards to the uncertainty around the mean predictions. Whale shark tourism is viewed as a potential means of protecting this threatened species, while also providing a sustainable livelihood for local communities and tourism providers. Management recommendations presented offer suggestions on how to tackle concerns over proximity distances and links to disturbance. Additionally judgments for future research endeavors into assessing both the impacts of uncontrolled tourism and participants behaviour
Global error reduction in vision-based self-localization using a topological graph representation
A single-sensor self-localization system which uses a monocular camera and a set of artificial landmarks is presented herein. The system represents the surrounding environment as a topological map (or graph) where each node corresponds to a marker (i.e., artificial landmark) and each edge corresponds to the existence of a relative pose between two markers. The edges are weighted based on an error metric (related to pose uncertainty) and a shortest path algorithm is applied to the map to compute the path corresponding to the least aggregate error. This path is used to localize the camera with respect to a global coordinate system whose origin lies on an arbitrary reference marker (i.e., the destination node of the path). Experimental results demonstrate the performance of the system in reducing the global error associated with large-scale localization