Accurate object tracking or target identification are key requirements in the automotive, consumer, and defence industries. These tasks require hardware to provide good quality images and accurate analysis routines to interpret the data. Here we will report on the use of next-generation single-photon avalanche detector (SPAD) array sensors combined with neural networks for high-speed three-dimensional imaging and object tracking. Such detectors enable three-dimensional imaging at high speeds and low light levels, and they can operate in a wide range of conditions and at large standoff distances. We will discuss the use of such detectors for tracking and monitoring airborne objects, such as drones. We will also discuss our recent work on human pose estimation, achieved from a low-cost SPAD time-of-flight sensor with only 4x4 pixels. Here we use neural networks to first increase the resolution of the data and then reconstruct the skeletal form of multiple humans in three dimensions. It is clear that the next generation of technology for object tracking and identification will use a combination of advanced imaging hardware and data fusion approaches. We will discuss our group's recent research in this area.
KEYWORDS: Ranging, Systems modeling, Imaging systems, Single photon, Data modeling, Time of flight imaging, Stereoscopy, Statistical modeling, Sensors, Picosecond phenomena
The recent development of single-photon avalanche diode (SPADs) arrays as imaging sensors with both picosecond binning capabilities and single photon sensitivity has led to the rapid development of time-of-flight imaging systems however, simulations of SPAD systems outside of the Poisson regime remain rare. Here we present a model for SPAD systems which combines single photon counting statistics with computational parallelization which together enable the efficient generation of photo-realistic SPAD data. We confirm the accuracy of out model by experimental verification. Further, we apply this simulator to the problem of drone identification, orientation, and, segmentation. The proliferation of semi-autonomous aerial multi-copters i.e. drones, has raised concerns over the ability of existing aerial detection systems to accurately characterize such vehicles. Here, we fuse the 3D imaging of SPAD sensors with the classification capabilities of a bespoke convolutional neural network (CNN) into a system capable of determining drone pose in flight. To overcome the lack of publicly available training data we generate a photo-realistic dataset to enable the training of our network. After training, we are able to predict the roll, pitch, and yaw of the several different drone types with an accuracy greater than 90%.
The recent development of single-photon avalanche diode (SPADs) arrays as imaging sensors with both picosecond binning capabilities and single photon sensitivity has led to the rapid development of time-of-flight imaging systems. When used in conjunction with a synchronised light source these sensors produce a 3D image. Here, we apply this 3D imaging ability to the problem of drone identification, orientation, and, segmentation. The proliferation of semi-autonomous aerial multi-copters i.e. drones, has raised concerns over the ability of existing aerial detection systems to accurately characterise such vehicles. Here, we fuse the 3D imaging of SPAD sensors with the classification capabilities of a bespoke convolutional neural network (CNN) into a system capable of determining drone pose in flight. To overcome the lack of publicly available training data we generate a photorealistic dataset to enable the training of our network. After training, we are able to predict the roll, pitch, and yaw of the several different drone types with an accuracy greater than 90%.
Single-photon detector array technologies have advanced significantly in recent years. Cameras now exist that are not only sensitive to single photons but the individual pixels in the sensor provide photon time-of-arrival information the picosecond regime. Such unprecedented sensitivity and temporal resolution opens up a number of exiting new applications, such as light-in-flight imaging, looking around corners with laser echoes, and seeing through dense scattering media. I will discuss the recent developments of the camera technology and discuss our latest results. I will give details of our latest field trials, where we have been using single-photon detector array sensors to see through fog and smoke. I will also discuss our latest results for high-speed imaging in three dimensions. The latest sensor is able to capture 3D data at frame rates greater than 1000 frames per second. This technology is relevant for the analysis of rapidly changing systems where three dimensional information is necessary.
Since its first demonstration in 1995, ghost imaging has provided amazing insights into both classical and quantum physics as well as having found application in, for example, microscopy and imaging under low light conditions. Traditional ghost imaging uses correlations between two photons to reconstruct an image of an object from two systems which each individually know nothing about the object. In the quantum case, the state of the two photons is typically a symmetric, entangled state. Here we investigate the effect that changing the two-photon state's symmetry has on the reconstructed object, by using Dove prisms and a Hong-Ou-Mandel filter. Interestingly, it appears that post-selecting on the anti-symmetric Bell state results in a `double image': a juxtaposition of the original image rotated both clockwise and anti-clockwise. Furthermore, we consider a 4-photon experiment in which two photons, which originate from different entanglement sources and are hence completely independent initially, acquire correlations by way of entanglement swapping via appropriate post-selection on the remaining two photons. In such a setup, post-selecting on the symmetric Bell states results in the original object, but post-selecting on the anti-symmetric Bell state results in a contrast-reversed image of the object. These studies highlight the fundamental importance that state symmetry plays in quantum imaging.
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