Calum G. Blair

Learn More
This paper presents a new implementation, with complete analysis, of the processing operations required in a widely-used pedestrian detection algorithm (the histogram of oriented gradients (HOG) detector) when run in various configurations on a heterogeneous platform suitable for use as an embedded system. The platform consists of field-programmable gate(More)
Object detection in modalities such as synthetic aperture sonar (SAS) is affected by the difficulty of acquiring a large number of training samples. If object classes not present in the training dataset are detected during testing, they can be mis-classified as one of the training classes. This increases overall false alarm rate and affects operator(More)
This paper presents a new implementation, with complete analysis, of the processing operations required in a widely-used pedestrian detection algorithm (the Histogram of Oriented Gradients detector) when run in various configurations on a heterogeneous platform suitable for use as an embedded system. The platform consists of FPGA, GPU and CPU and we detail(More)
State-of-the-art pedestrian detectors are capable of finding humans in images with reasonable accuracy. However, accurate object detectors such as Integral Channel Features (ICF) do not provide good reliability; they are unable to identify detections which they are less confident (or more uncertain) about. We apply existing methods for generating(More)
In surveillance and scene awareness applications using power-constrained or battery-powered equipment, performance characteristics of processing hardware must be considered. We describe a novel framework for moving processing platform selection from a single design-time choice to a continuous run-time one, greatly increasing flexibility and responsiveness.(More)
Field-programmable gate arrays (FPGAs) and graphics processing units (GPUs) are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time, and detection accuracy), and this decision is normally made once at design time. All three of the(More)
Gaussian Process classification (GPC) allows accurate and reliable detection of objects. The high computational load of squared-error or radial basis function kernels limits the applications that GPC can be used in, as memory requirements and computation time are both limiting factors. We describe our version of accelerated GPC on GPU (Graphics Processing(More)
  • 1