Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications

Compute and memory demands of state-of-the-art deep learning methods are still a shortcoming that must be addressed to make them useful at IoT end-nodes. In particular, recent results depict a hopeful prospect for image processing using Convolutional Neural Netwoks, CNNs, but the gap between software and hardware implementations is already considerable for IoT and mobile edge computing applications due to their high power consumption. This proposal performs low-power and real time deep learning-based multiple object visual tracking implemented on an NVIDIA Jetson TX2 development kit. It includes a camera and wireless connection capability and it is battery powered for mobile and outdoor applications. A collection of representative sequences captured with the on-board camera, dETRUSC video dataset, is used to exemplify the performance of the proposed algorithm and to facilitate benchmarking. The results in terms of power consumption and frame rate demonstrate the feasibility of deep learning algorithms on embedded platforms although more effort to joint algorithm and hardware design of CNNs is needed.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Evaluación de desempeño de redes convolucionales sobre arquitecturas heterogéneas para aplicaciones en robótica autónoma

Humanoid robots find application in human-robot interaction tasks. However, despite their capabilities, their sequential computing system limits the execution of computationally expensive algorithms such as convolutional neural networks, which have demonstrated good performance in recognition tasks. As an alternative to sequential computing units, Field-Programmable Gate Arrays and Graphics Processing Units have a high degree of parallelism and low power consumption. This study aims to improve the visual perception of a humanoid robot called NAO using these embedded systems running a convolutional neural network. The methodology adopted here is based on image acquisition and transmission using simulation software: Webots and Choreographe. In each embedded system, an object recognition stage is performed using commercial convolutional neural network acceleration frameworks. Xilinx® Ultra96™, Intel® Cyclone® V-SoC and NVIDIA® Jetson™ TX2 cards were used, and Tinier-YOLO, AlexNet, Inception-V1 and Inception V3 transfer-learning networks were executed. Real-time metrics were obtained when Inception V1, Inception V3 transfer-learning and AlexNet were run on the Ultra96 and Jetson TX2 cards, with frame rates between 28 and 30 frames per second. The results demonstrated that the use of these embedded systems and convolutional neural networks can provide humanoid robots such as NAO with greater visual recognition in tasks that require high accuracy and autonomy.Los robots humanoides encuentran aplicación en tareas de interacción humano-robot. A pesar de sus capacidades, su sistema de computación secuencial limita la ejecución de algoritmos computacionalmente costosos, como las redes neuronales convolucionales, que han demostrado buen rendimiento en tareas de reconocimiento. Como alternativa a unidades de cómputo secuencial se encuentran los Field Programmable Gate Arrays y las Graphics Processing Unit, que tienen un alto grado de paralelismo y bajo consumo de energía. Este trabajo tuvo como objetivo mejorar la percepción visual del robot humanoide NAO utilizando estos sistemas embebidos que ejecutan una red neuronal convolucional. El trabajo se basó en la adquisición y transmisión de la imagen usando herramientas de simulación como Webots y Choreographe. Posteriormente, en cada sistema embebido, se realizó una etapa de reconocimiento del objeto utilizando frameworks de aceleración comerciales de redes neuronales convolucionales. Luego se utilizaron las tarjetas Xilinx Ultra96, Intel Cyclone V-SoC y Nvidia Jetson TX2; después fueron ejecutadas las redes Tinier-Yolo, Alexnet, Inception V1 y Inception V3 transfer-learning. Se obtuvieron métricas en tiempo real cuando Inception V1, Inception V3 transfer-learning y AlexNet fueron ejecutadas sobre la Ultra96 y Jetson TX2, teniendo como intervalo entre 28 y 30 cuadros por segundo. Los resultados demostraron que el uso de estos sistemas embebidos y redes neuronales convolucionales puede otorgarles a robots humanoides, como NAO, mayor reconocimiento visual en tareas que requieren alta precisión y autonomía. &nbsp

Embedded Vision Systems: A Review of the Literature

Over the past two decades, the use of low power Field Programmable Gate Arrays (FPGA) for the acceleration of various vision systems mainly on embedded devices have become widespread. The reconfigurable and parallel nature of the FPGA opens up new opportunities to speed-up computationally intensive vision and neural algorithms on embedded and portable devices. This paper presents a comprehensive review of embedded vision algorithms and applications over the past decade. The review will discuss vision based systems and approaches, and how they have been implemented on embedded devices. Topics covered include image acquisition, preprocessing, object detection and tracking, recognition as well as high-level classification. This is followed by an outline of the advantages and disadvantages of the various embedded implementations. Finally, an overview of the challenges in the field and future research trends are presented. This review is expected to serve as a tutorial and reference source for embedded computer vision systems