A Bottom-up Approach for Visual Object Recognition on FPGA based Embedded Multiprocessor Architecture

Abstract

Abstract-This paper presents an object recognition approach of outdoor autonomous systems identifying the nature of the interested object when observing an image. Therefore, seeking for effective and robust recognition method, the proposed approach is performed using a novel saliency based feature detector/descriptor which is combined with an object classifier to identify the nature of objects in an indoor or an outdoor environment. As known, bottom-up visual attention computational models need a considerable computational power and communication cost. A major challenge in this work is to deal with such image processing applications managing a large amount of the information processing and to work within real-time requirements by improving the processing speed.Based on interesting approach designing specific architectures for parallelism, this paper presents a solution for rapid prototyping of saliency-based object recognition applications. In order to meet computation and communication requirement, the developed pipelined architectures are composed of identical processing modules which can work concurrently with distributed memories and compute in parallel several sequential tasks with a high computational cost. We present hardware implementations with performance results on an Xilinx System-on-Programmable Chip (SoPC) target. The experimental results including execution times and application speedups as well as requirements in terms of computing resources show that the proposed homogeneous network of processors is efficient for embedding the proposed image processing application.

Embedded Mission Decision-Making based on Dynamic Decision Networks in SoPC Platform

“…14), image stabilization [11]), pose estimation [12] (Fig. 15), object recognition [14], object tracking [13]), region labeling and path planning. These applications based image processing are already embedded on-board FPGA in our autonomous UAV.…”

Object’s Shape Recognition using Local Binary Patterns

“…A bottom-up approach is presented in [9] to identify nature of fascinated objects. To develop an operative and vigorous identification technique, the suggested methodology is accomplished by extracting the features of objects.…”