## 4 Experiment Result and Analysis
### The Working Mechanism of Contrast Detection Channel  
In our work, we design a contrast information detection channel to help extract the contrast information of small features and combine it with a motion detection channel for discriminating small moving objects and background false positives. To confirm the validity of adding channels, we first explain the working mechanism of the contrast detection channel, then assess the performance including and excluding the contrast detection channel. Figure 11a,b gives the contrast information of the small target and fake feature, where Figure 11a,b denotes the contrast outputs to the small target and fake feature on time \(t_{0}=500\) ms, and Figure 11c,d represents the change curve of contrast during the period. As can be seen, the contrast information of the small object and fake feature in fixed time \(t_{0}=500\) ms is cluttered and it is helpless to identify small target motion and fake feature motion. Meanwhile, both the contrast fluctuation curves, of the true object and background false positives, across time are discriminative. More specifically, the contrast fluctuation curve of the small object exhibits a clear trend of ups and downs over time; on the contrary, the contrast change curve of a fake feature remains invariable. As a result, small objects can be separated from background fake features based on the intrinsic differences in contrast variation over time. The contrast variance is applied to characterize these inherent differences in the contrast change curve, helping us identify small moving objects among the background fake features. Figure 12a,c presents the trajectories of small objects (blue curve) and fake features (red curve) recorded by our approach, excluding the contrast detection channel with diverse detection parameters \(\beta\). Figure 11: (a,b) Contrast information of the small target and fake features at a specified time point, \(t_{0}=500\) ms. (c,d) Temporal evolution of contrast information of the small target and fake features, observed over the time interval \([0,800]\) ms.  
Figure 10: (a) STMD neural response. (b) STMD neural response of fake target (dotted line). It can be easily observed that these trajectory results consistently include a high number of false positives trajectories, and the number of false positives trajectories gradually decreases as the threshold parameter increases. Meanwhile, the motion trajectories of small targets and fake features recorded by the \(\mathcal{CI}\)STMD model including the contrast detection channel across various thresholds parameters \(\beta\) are presented in Figure 12d-f. As observed, a significant number of fake features is eliminated by the contrast detection channel at different thresholds. As shown in Figure 13a-c, the trajectories of movement identified by the ESTMD, DSTMD, and \(\mathcal{F}\)STMD methods are presented. It is evident that their logged data are exceedingly convoluted because of the multitude of spurious attributes, which renders the detection of small targets from these results unfeasible. It is clear that, owing to the interference of numerous false features, the original detection results are extremely chaotic, making it difficult to accurately extract the small target. By adjusting the contrast variance threshold \(\gamma\), the \(\mathcal{CI}\)STMD effectively suppresses false features, improving the purity of the detection results. As shown in Figure 13d-f, as the value of \(\gamma\) increases, the detected motion trajectories become clearer, and the interference gradually decreases. We further show the contrast variance curves of a small moving object and background fake feature over time \(t\) in Figure 14a,b. As shown in Figure 14a,b, the contrast variance of the small object exhibits a trend of initially increasing and then stabilizing, ultimately maintaining a high level, but the contrast variance to the fake feature is close to zero and hardly changes over the time \(t\). We present the output of the STMD after thresholding in Figure 14c,d.