## 3 The CFSR Method
### Fine-Grained Reconstruction  
As shown in Figure 2c, the difference between adjacent inter-spike intervals (DAISI) of the static and dynamic regions are also different. After coarse-grained reconstruction,  
Figure 2: The temporal distribution of index of number of ISI (INI) under different brightness and motion state. From left to right: (**a**) value of interval; (**b**) cardinality of interval; (**c**) the difference between adjacent inter-spike intervals (DAISI). From top to bottom, the results of different regions are marked in different colors, where the red and gray belong to the dynamic, and the yellow and blue ones are the static, respectively. the fine-grained distinction is operated on the dynamic spikes and the coarse-grained scene reconstruction, as illustrated in Figure 3. We can first obtain the coarse-grained reconstruction scene \(f_{t_{m}}(x,y)\) of consecutive frames in the fixed time interval \(T\). We utilize the Mixture of Gaussian model (MOG) based background subtraction method [32] to distinguish the moving object from the dynamic region to obtain the background image \(B(x,y)\). Then, we can calculate the differential image \(D^{\prime}_{t_{m}}(x,y)\) via  
\[D^{{}^{\prime}}_{t_{m}}(x,y)=|f_{t_{m}}(x,y)-B(x,y)|. \tag{8}\]  
We set a threshold \(\varphi_{0}\) and obtain the binary image \(R^{\prime}_{t_{m}}(x,y)\) by the threshold processing for the differential image \(D^{\prime}_{t_{m}}(x,y)\),  
\[R^{{}^{\prime}}_{t_{m}}(x,y)= \begin{cases}1&D^{{}^{\prime}}_{t_{m}}(x,y)\geq \varphi_{0}\\ 0&D^{{}^{\prime}}_{t_{m}}(x,y)<\varphi_{0}.\end{cases}  \tag{9}\]  
The values of 1 and 0 represent the dynamic object and the static region, respectively. However, the distinction of the edge of the dynamic object is not well-defined. We then use the difference between adjacent inter-spike intervals (DAISI) to judge the edge of the dynamic object. We observe that when the average brightness intensity changes greatly in a short time, the current spikes are the edge of the dynamic region, assuming that the brightness of the dynamic region is inconsistent with that of the static region, as shown in Figure 2c. There is an edge of the dynamic region when a change in that log average luminance intensity \(\bar{I}_{m}\) of \(\Delta t_{m}\) exceeds a threshold \(\delta\),  
\[\left|\log(\overset{-}{I}_{m})-log(\overset{-}{I}_{m-1})\right|\geq\delta, \tag{10}\]  
where \(\bar{I}_{m}\) and \(\bar{I}_{m-1}\) can be estimated by Equation (2). Correspondingly, the edge of the dynamic region exists when a change in the log ISI exceeds a threshold \(\varphi\),  
\[\left|\log(\Delta t_{m})-log(\Delta t_{m-1})\right|\geq\varphi. \tag{11}\]  
Figure 3: Fine-grained spike state distinction for the dynamic region and the static region. Spike train with binary marks (dynamic or static) and the CSR are as inputs to the fine-grained distinction. The Mixture of Gaussian model (MOG) based background subtraction method is utilized on the CSR for dynamic object judgment, and the difference between adjacent inter-spike intervals (DAISI) is utilized to judge the edge of the dynamic object. Finally, the edge information obtained by the DAISI method is used to refine the dynamic and static spikes. When a spike is generated at time \(t_{m}\), Equation (10) can represent the motion state at the current time well, as shown in Figure 3. However, when there is no spike generated at time \(t_{m}\), we have difficulty judging the motion state at the current moment \(t_{m}\) by the difference between two adjacent ISIs, as shown in Figure 3. For this case, we utilize the difference among three adjacent ISIs to judge the edge of the moving region via  
\[\Big{\{}|\text{log}(\Delta t_{m})-\text{log}(\Delta t_{m-1})|\big{[}\bigcap| \text{log}(\Delta t_{m})-\text{log}(\Delta t_{m+1})|\big{\}}\geq\varphi. \tag{12}\]  
Assume \(t_{a}\) is the time point when the spike is generated before \(t_{m}\). We can assess whether a one-bit spike is generated or not from \(t_{a}\) to \(t_{m}\) via Equation (1).