## 1 Introduction  
The monitoring of Respiratory Rate (RR) is a relevant factor in medical applications and day-to-day activities. Contact sensors have been used mostly as a direct solution and they have shown their effectiveness, but with some disadvantages. In general, the main inconveniences are related to the correct and specific use of each contact sensor, the stress, pain, and irritation caused, mainly on some vulnerable skins, like neonates and burns patients [1]. For a review of contact-based methods and comparisons see [2]. Contactless breathing monitoring is a recent research interest for clinical and day-to-day applications; a review, and comparison of contactless monitoring techniques can be seen in [3]. In this paper, the literature review is restricted to respiratory activity and the works concerning the detection of cardiac activity are not included voluntarily. There are three main categories for contactless RR monitoring methods. The first group includes non-image-based proposals, like radar sensor approaches [4; 5] and sound-based approaches [6]. The main disadvantage of radar methods is that the antenna must be in front of the thoracic area, a restriction that cannot always be met [7], and for sound-based approaches ambient noise remains a difficulty for the extraction of signal [3]. Other recent approaches include smart textiles for respiratory monitoring with evident restriction in some medical applications [8]. The second group includesdifferent kinds of image sensing. Thermal images [9; 10; 11] measure the temperature variations between inhalation and exhalation phases with it not working if the nasal area is not visible. In recent years, several works have used the photoplethysmography technique, employed initially to measure cardiac frequency [12], to measures skin blood changes to track RR [13; 14; 15; 16; 17; 18]. Some works such as [18] train a CNN using respiratory raw signal as reference and a skin reflection model to represent the color variations of the image sequence as input. This technique is robust for extracting signal in both dark and light lighting conditions [14]; the motion artifacts can be corrected [17] and can be introduced to a multi-camera system for tracking cardiorespiratory signals for multiple people [19]. This method is promising; however, the skin must always be visible, a condition that is not met in some positions of the subject. Other methods directly extract the respiratory signal from the motion detected on the RGB video. Different strategies to track motion are proposed. For example, [PERSON] et al. [20] employed frame subtraction and temporal filtering to extract signal, and [PERSON] et al. detected the motion of the face and the shoulders for a spirometry study [21]. Several works have used magnification video motion technique to track subtle motions [22]. This last technique allows revelation of invisible motions due to respiratory rhythm [23; 24; 25; 26; 27]. The third category uses hybrid techniques applying image-based and non-image-based approaches. For example, in [28], a sleep monitoring system using infrared cameras and motion sensors is proposed. Motion magnification methods compute respiratory rate by detecting motions of the thoracic cavity. Their main advantages are that they need only an RGB camera, and the measurement can be taken in different thorax cavity regions, as opposed to thermal and photoplethysmography techniques that require a specific region to extract signal. Motion magnification can be categorized according to the magnification of the amplitude [23; 24; 29; 30] or the phase of the signal [25; 26]. It is shown in [31] that phase amplitude produces a lower noise level in magnified signal compared to amplitude magnification; however, algorithm complexity is higher. Phase magnification also allows discrimination of large motions not related to respiratory activity as shown by [PERSON] et al. [25] or a motion compensation strategy to stabilize RR reported by [26]. Different decomposition techniques are used to carry out magnification.