## 4 Experiments  
In our experiments, we compare the tracking results of PS-L1 tracker with L1 tracker and APG-L1 tracker, besides blue, red and green bounding boxes are used to denote them respectively. All algorithm are evaluated on the AMCOM FLIR dataset which comprises FLIR sequences in grayscale format (\(128\times 128\) pixels). Algorithm parameters are set as follows.The template size is \(10\times 10\) and variances of affine parameters are \((0.03,0.0005,0.0005,0.03,1,1)\). Besides, \(10\) target templates are used and the number of state particles is \(400\). The angle threshold \(\theta_{thr}\) is \(40\) to determine whether to update a target template. Several numerical experiments are performed on the personal computer with 2.67 GHz Inter(R) Core(TM) I3 CPU and 2 GB memory. Besides, Windows 7 and MATLAB R2010a are installed. The \(\ell_{1}\) minimization problem is solved by SPAMS software package for the PS-L1 tracker. The first experiment is performed on the sequence of LW-16-08 and the results are shown in Figure 1. The challenge of this sequence is the disturbance of similar target. A stationary target is at the center position whose size becomes larger with time, and another target with similar appearance moves to the existing one. Partial occlusion happens when they are close enough. The experimental results show that both L1 tracker and APG-L1 tracker fail after the two targets encounter though the latter can handle occlusion. The PS-L1 tracker also fails for a short time, it recovers soon to lock on the target during the rest frames of the sequence. The second experiment is performed on the sequence of LW-17-20 and the results are shown in Figure 2. The challenge of this sequence is the obvious variance of size and pose. A target moves along the road then turns right. It can be seen that both L1 tracker and APG-L1 tracker only lock on a little part of the target after its size becomes larger. For PS-L1 tracker, it can lock on the whole target though more background pixels are contained. The reason is that all the three trackers use the generative model thus various target appearance and background are hard to discriminate. In fact, holistic templates based on raw intensity values do not take large appearance variability into account which do not perform well in a situation of obvious variance of pose. The third experiment is performed on the sequence of LW-19-NS and the results are shown in Figure 3. The challenges of this sequence are occlusion and background clutter. Two targets move along the road. At first, occlusion happens due to vehicle exhaust; thus, only part of the first target is visible. All the three trackers perform well before the target turns right because sparse representation is insensitive to occlusion. Then the target appearance changes with time and the background clutter also causes disturbance. It can be seen that our tracker is more stable to lock on the target while the other two lose it. The fourth experiment is performed on the sequence of LW-18-15 and the results are shown in Figure 4. The challenge of this sequence is low contrast. A small target becomes close to the infrared sensor whose size becomes larger with time. The target is small and dark, and it is very hard to discriminate the target from background. At first, all the three trackers perform well because sparse  
Figure 1: Tracking results of three trackers for sequence LW-16-08. Figure 3: Tracking results of three trackers for sequence LW-19-NS. Figure 2: Tracking results of three trackers for sequence LW-17-20. representation can extract the principal features. Then the target appearance changes with time, the APG-L1 tracker even fails to track the target. Although the L1 tracker and PS-L1 tracker can lock on the target through the whole sequence, they do not perform well in estimation of target size. For the L1 tracker, many background pixels are included, and only a little part of target is locked for the PS-L1 tracker.