Algorithm 3 Independent object tracking

Input: ${\mathit{O}}_{\mathit{t}-1},{\mathit{C}}_{\mathit{t}}$ Output: ${\mathit{O}}_{\mathit{t}}$ 1: $\mathit{O}\leftarrow \mathrm{\varnothing }$ 2: $M=size\left(O_{t-1}\right)$                      ► number of objects at previous frame 3:  $N=size\left(C_t\right)$                       ► number of objects at current frame 4: $Array \leftarrow zeros(M, N)$              ► An array that stores distances between all objects 5: $\mathit{f}\mathit{o}\mathit{r} i \mathit{i}\mathit{n} O_{t-1}:$ 6: $\mathit{f}\mathit{o}\mathit{r} j \mathit{i}\mathit{n} C_t:$ 7: $Array\left[i, j\right]=distance(O_{t-1}\left[i\right], C_t\left[j\right])$ 8: $\mathit{f}\mathit{o}\mathit{r} k \mathit{i}\mathit{n} C_t:$ 9: $\mathit{i}\mathit{f} ID\left[k\right] \mathit{i}\mathit{s}\mathit{n}\mathit{o}\mathit{t} set:$ 10: $\mathit{i}\mathit{f} Array\left[i, k\right]==\mathrm{m}\mathrm{i}\mathrm{n}\left(Array\left[i, :\right]\right):$            ► object k: closest object to object i 11: $\mathit{i}\mathit{f} Array\left[i, k\right]<distThresh:$                 ► if below distance threshold: 12: $ID\left[k\right]\leftarrow ID\left[i\right]$              ► assume k and i is the same object (tracking success) 13: $Tracked length of ID\left[k\right]+=1$ ► Increase tracked length 14: $\mathit{b}\mathit{r}\mathit{e}\mathit{a}\mathit{k}$ 15: $\mathit{e}\mathit{l}\mathit{s}\mathit{e}:$ 16: $mark ID\left[i\right] as lost$                  ► Mark as lost if no object is nearby 17: $\mathit{f}\mathit{o}\mathit{r} k \mathit{i}\mathit{n} C_t:$ 18: $\mathit{i}\mathit{f} ID of k \mathit{i}\mathit{s}\mathit{n}\mathit{o}\mathit{t} set:$ 19: $assign new ID to k$                ► assign new ID if new object is detected 20: $tracked length of ID\left[k\right]\leftarrow 0$ 21: $\mathit{i}\mathit{f} tracked length of ID\left[k\right]>noiseThresh:$            ► If tracked longer than threshold 22: $ID\left[k\right]\leftarrow mark as true object$ ► Approve as true object 23: $add position and ID of k to {\mathit{O}}_{\mathit{t}}$