Proposta de Implementação em Hardware do Algoritmo de Otsu Aplicado ao Rastreamento em Tempo Real de Vermes

This work proposes an implementation in Field Programmable GateArray (FPGA) of the Otsu’s method applied to real-time trackingof worms called Caenorhabditis elegans. Real-time tracking is necessaryto measure changes in the worm’s behavior in response totreatment with Ribonucleic Acid (RNA) interference. Otsu’s methodis a global thresholding algorithm used to define an optimal thresholdbetween two classes. However, this technique in real-time applicationsassociated with the processing of high-resolution videoshas a high computational cost because of the massive amount ofdata generated. Otsu’s algorithm needs to identify the worms ineach frame captured by a high-resolution camera in a real-timeanalysis of the worm’s behavior. Thus, this work proposes a highperformanceimplementation of Otsu’s algorithm in FPGA. Theresults show it was possible to achieve a speedup up to 5 timeshigher than similar works in the literature

Real Time Vision System for Obstacle Detection and Localization on FPGA

International audienceObstacle detection is a mandatory function for a robot navigating in an indoor environment especially when interaction with humans is done in a cluttered environment. Commonly used vision-based solutions like SLAM (Simultaneous Localization and Mapping) or optical flow tend to be computation intensive and require powerful computation resources to meet low speed real-time constraints. Solutions using LIDAR (Light Detection And Ranging) sensors are more robust but not cost effective. This paper presents a real-time hardware architecture for vision-based obstacle detection and localization based on IPM (Inverse Perspective Mapping) for obstacle detection, and Otsu's method plus Bresenham's algorithm for obstacle segmentation and localization under the hypothesis of a flat ground. The proposed architecture combines cost effectiveness, high frame-rate with low latency, low power consumption and without any prior knowledge of the scene compared to existing implementations