## 2 On-Device Object Detection for Context Awareness in Ambient Intelligence Systems
Intelligent transportation systems (ITS) have lately attracted considerable research interest driven by the modernization of transportation infrastructures and the development of autonomous driving and its supporting technologies. The reviewed AmI literature is good evidence of that. Although the works specifically identified along those lines [64; 68; 69; 77; 78; 79; 82; 85; 96; 106; 102; 105] cover only a limited subset of the broad spectrum of applications resulting from the technological progress in the field, they provide good intuition on the relevance of object detection as a key factor for making both vehicles and infrastructures safer, more efficient, comfortable and reliable. In particular, in-vehicle ITS systems [64; 77; 78; 79; 85; 96; 102; 105] (commonly known as Advanced Driving Assistance Systems or ADAS) stand out in the analysis as the central focus of interest. Such systems embed detection solutions conceived as safety mechanisms for monitoring both driver operations [77; 78; 85], preventing distractions, and ultimately, the loss of control of the vehicle, as well as on-road events [64; 79; 96; 102; 105], being the latter mainly implemented nowadays in the form of a warning instrument triggered in situations of potential collisions [79; 96] or infractions [106], but also designed towards decision-making support in future autonomous vehicles [64; 102; 105]. For its part, regarding conventional ITS alternatives [68; 69; 82], the different approaches identified are aimed at road traffic control and early response to emergencies, applications in which the use of UAVs has been intensively explored [68; 69] due to their capabilities to record and communicate information in a non-intrusive way, as well as to recognize areas inaccessible by other means. A key challenge in this context lies in UAVs' built-in computing components since they have to both process the acquired images with almost no latency to make critical navigation decisions [68] while consuming as little power as possible to minimize its impact on the battery life and hence on the system's flight time [69]. As with ITS, surveillance systems have similarly undergone a dramatic development, pushed by the advancement of information and communication technologies. Like the former, they require a real-time capacity to perform key tasks, such as monitoring and target tracking. In this regard, the studies analyzed within the domain [67; 70; 75; 89; 90; 95; 98; 104; 109] reveal a large body of works focused on the exploitation of efficient CNNs for human detection in a wide range of scenarios: street monitoring in urban areas [67], home surveillance [75], subway [95] and railway security [90], people search and rescue in natural disasters [104], and recognition of specific human behaviors [89]. Besides low latency, there is a couple of additional factors to be considered when designing such systems that have emerged in the review: privacy [70] and cost [75]. Individuals' privacy and anonymity should be a central tenet of surveillance systems. However, RGB cameras commonly used for such purposes are too invasive, and for that reason, we see how authors, such as [PERSON] et al. [70] explore the exploitation of alternatives, such as raw depth data. Furthermore, surveillance has traditionally been extremely important for business and home security, and while there is a decent number of technological solutions in the market, most of them rely on expensive proprietary cameras. Low-cost IoT hardware platforms, as suggested by [75], might be a perfectly valid approach to building solutions tailored to more austere budgets. Surveillance and development of low-cost systems can be also observed in smart farming literature, the third of the five main AmI domains identified. In particular, both of those elements are present in [PERSON] et al. 's work [93].