in $t+\delta$ by searching around its previous location at time $t$. We obtain our search region by enlarging the original detection $target^t$ by a factor $r$: $search^{t+\delta}=[x^t, y^t, w^t*r, h^t*r]$. Then, we extract the target template feature map $\mathbf{f}_{target^t}$ and that of its corresponding search region: $\mathbf{f}_{search^{t+\delta}}$. Finally, we predict the status of the person at time $t+\delta$ as follow: $$(\hat{v}, \hat{m}) = \mathbb{\phi}(\mathbf{f}_{target^t}, \mathbf{f}_{search^{t+\delta}})
    \label{equation: siamese_track}$$

where $\hat{m}$ is the estimated object motion parameterized as $[\frac{x^{t+\delta} - x^t }{w^t}, \ \frac{y^{t+\delta}-y^t}{h^t}$ $\text{log}\frac{w^{t+\delta}}{w^t}], \ \text{log}\frac{h^{t+\delta}}{h^t}]$, $\hat{v}$ indicates whether the person is visible or not at time $t+\delta$, which is essential to ensure that a track does not continue if its person is no longer in view; and $\phi$ is a fully connected network inspired by GOTURN [@held2016learning]. In order for this to work, we flatten $\mathbf{f}_{target^t}$ and $\mathbf{f}_{search^{t+\delta}}$ and concatenate them before inputting them into $\phi$. We chose this specific network for $\phi$ because it is accurate and extremely efficient, but note any Siamese-based SOT [@held2016learning; @li2018high; @zhu2018distractor; @li2019siamrpn++; @wang2019fast] can be employed to parameterize $\phi$. This is especially appealing, as our model can directly benefit from future advances in SOT.\
[**Training.**]{} We train our network $\phi$ using a combination 