## 1 Introduction  
Today ambient intelligence (Aml) systems are experiencing an unprecedented growth momentum essentially due to the thrust and fast development of two of their main enabling technological forces: the so-called Internet of Things (IoT), focused on the exploitation of networked sensor infrastructures to remotely gather data and enable data exchange between several distributed ends and Artificial Intelligence (AI), more oriented to the use of gathered data and the subsequent distillation of knowledge necessary to make Aml systems adaptable and \"aware\" of their surroundings. Both fields, IoT and AI have experienced outstanding progress almost side by side in the last two decades, a fact in no case fortunate, but rather the result of a continuous interaction that is still going on nowadays. Methods and technologies developed within AmI under the IoT paradigm facilitate communication and data exchange among the different devices that comprise the so-called intelligent environments, enabling the creation and proper exploitation of new network architectures based on connected sensing instruments and resource-constrained energy-efficient end devices (i.e., embedded devices and mobile devices). Efforts made in that regard have been primarily aimed both at the design and deployment of faster and more efficient network infrastructures, as well as at the development of more accurate sensing platforms and more powerful computing hardware, enabling the collection and storage of large volumes of data and consequently, the support of increasingly sophisticated AI techniques, from traditional machine learning (ML) methods, all the way to more recent deep learning (DL) approaches. Modern advanced algorithmic solutions can process sensed data and derive high-level knowledge to allow intelligent computational systems to recognize and understand both user activity and behavior, as well as the different phenomena around them to extract context-tailored knowledge and consequently, provide more effective support. Artificial visual perception techniques, particularly object detection methods, have emerged as some of the most relevant enabling factors of that user-centered intelligence, providing a better understanding of the environment and the entities in it, thus constituting a fundamental pillar for vision tasks in the AmI context, such as object tracking [1] and human action recognition [2; 3], among others. Despite the qualitative leap forward that the recent wide adoption of convolutional neural networks (CNNs) brought accuracy-wise and the countless research efforts mobilized in the last decade in that direction, object detection remains a highly complex problem. CNNs, as a specialized type of DL networks, are computationally expensive [4; 5] and require a large memory footprint. Although cloud platforms have shown to be scalable and powerful enough to meet those needs, leveraging such infrastructures typically involves deploying neural networks as remote services, demanding a virtually persistent connection with the server-side. That usually leads to latency (network speed + processing time) and security limitations that make those solutions unsuitable for conventional AmI scenarios where the system response time must be as short as possible, or data privacy needs to be ensured. As an alternative to the cloud, many authors have focused their research on local processing strategies in response to the limitations mentioned, resulting in a novel DL trend called on-device machine learning. Such an approach pursues more compact and efficient models directly deployable into IoT devices, reducing the server-side computational load, the data traffic between endpoints, and the associated latency. Thus, it makes it possible to incorporate a layer of intelligence into the different end devices in AmI systems and as a result of the latter, it enables a more fluid experience better tailored to end users' needs, without compromising the privacy of their data.