Hardware-oriented Adaptation of a Particle Swarm Optimization Algorithm for Object Detection

In this paper we propose a simplified, hardware-oriented algorithm for object detection, based on particle swarm optimization. Starting from an algorithm coded in a high-level language which has shown to perform well, both in terms of accuracy and of computation efficiency, the simplified version can be implemented on an FPGA. After describing the original algorithm, we describe how it has been simplified for hardware implementation. We show how the intrinsic modularity of the algorithm permits to define a general core, independent of the specific application, which implements object search, along with a simple application specific-module, which implements a problem-dependent fitness function. This makes the system easily reconfigurable when switching between different object detection applications. Finally, we show some examples of application of our algorithm and discuss about possible future developments.