## 6 Non-Stationary Object FoR Estimation  
In this section, we discuss the schematics of non-stationary person identification by 3 FFs UAVs. We consider that UAV swarms \(\{U_{1},U_{2},\ldots,U_{k}\}\) are visually tracking objects such as boats, trains, and flying objects in which our object of interests (persons) are located. Thus, these persons fall under the non-stationary detection category. Figure 6 presents the scenario. In this, we consider that in the swarm network, we have \(a\) tracking UAVs, which track the geo-fencing location of the person (inside the object), also denoted as containers in our scheme. For geo-fencing, each person needs to keep the GPS sensor in ON mode, from where the \((lat,long)\) coordinates are located by satellite \(S\). \(S\) communicates with the mobile apps, which send the signals to the \(a\) UAVs. However, for accurate prediction of a person, multiple satellite nodes coordinate through the trilateration process, which reduces the biases in the distance computation of objects with the UAVs. In our case, we consider that moving containers have a velocity (with a relative direction towards the tracking UAV, or moving away from UAV). We denote the towards velocity as \(V_{ot}\) and receding object velocity as \(V_{or}\). Moreover, the object might have a rotational kinetic component, denoted by \(\psi_{0}\). We divide the estimation into FoR, where two types of FoRs are present--actual FoR (AFoR) and relative FoR (RFoR). In the AFoRs, the person is stationary, but in the RFoRs, the person is moving with the linear and angular velocity of the container. Thus, we base our approach on the dual-model background subtraction method [53], where moving estimations are captured by UAV sensor cameras, and thus accurate location in the container is presented. Based on the AFoR, motion compensation is measured from the baseline (when AFoR and RFoR are the same). A sampling map \(S_{map}\) is formed, and the next map depends on the relative shift \(\Omega\) obtained from the previous map \(M_{i-1}\) to current \(Map_{i}\). A foreground probability map is formed by the YOLOv3 model, where the pixels \((x_{i},y_{i})\) in container FoR (map) are adjusted (subtracted) with the object displacement in a unit time \(t\). Thus, we compute the distance \(d\) that the containers move in a unit of time (\(v.t\)) and subtract the same from the temporal map sequence to obtain the stationary FoR. In terms of angular movements, the container can move in any of the X, Y, and Z axes, and thus the projective distance \(p_{d}\) is computed per unit of time to adjust the temporal map sequence. In terms of pixels, the temporal sequences \(T_{M}\) are denoted as follows. \[T_{M}=(1-\alpha_{T})T_{M}^{i-1}(p)+\alpha_{T}D^{i}(p) \tag{14}\]  
where \(i\) is the frame indexes captured by UAVs, and \(\alpha_{T}\) is the temporal learning rate of the model. \(p\) denotes the pixel values of bounding box estimation for the person and \(T_{M}^{i-1}\) denotes the temporal map matrix at the previous frame \((i-1)\). The next frame is corrected based on velocity estimation (both linear and angular). To match the spatial pixel property, we compute the coherency of spatial pixels as follows. \[S_{M}^{t}(p)=(1-\alpha_{s}S_{M}^{t-1}(p)+\alpha_{s}\frac{1}{n^{2}}\sum_{i\in N (p)}D^{t}(p) \tag{15}\]  
where \(N(p)\) denotes the neighboring map at spatial instance \((t-1)\). The foreground probability is denoted as follows. \[P_{FG}^{t}(p)=T_{M}^{t}(p).S_{M}^{t}(p) \tag{16}\]  
Figure 6: Non-stationery FoR estimation of persons with moving objects. where (.) denotes the convolutional operation. For bounding-box estimation by the model, the RFoR is constructed as a grid sequence ( \(q\times q\)) grid, where we fit the temporally adjusted frames in the grid for box estimation \(B_{e}\). We consider a total of \(p\) grids, denoted as \(\{G_{1},G_{2},\ldots,G_{p}\}\) constructed for the purpose.