## 2 Materials and Methods  
The background activity is caused by many factors such as charge injection, the leakage of the transistor section of the reset switch, and thermal noise. The location of the background activity is random and less frequent than real events. In addition, similar to the hot pixel of traditional image sensors, a similar situation exists in DVS. Because the pixel cannot be reset properly, the pixel continuously outputs events [20]. In the image, two kinds of noise exist at the same time. As shown in Figure 1, a filter is required to filter out these two kinds of events that should not occur. Figure 1: This is the data of a moving car. The sensor size is \(768\times 640\). There are real events and a lot of noise in the data. The color indicates the gray information of the event. [PERSON]'s team has confirmed through testing that the BA events from the DVS can be assumed to be drawn from a Poisson distribution [21]. Calculating the probability that a single pixel will generate BA numbers within a finite time interval can be performed by the Poisson process:  
\[\text{P}[\text{N}(\text{t})\ =\text{n}]\ =\ \frac{\left(\lambda t\right)^{n}}{n!}e^{- \lambda t}. \tag{1}\]  
In Equation (1), \(t\) is the time interval, \(n\) is the number of BAs reached in the time interval, and \(\lambda\) is the average rate of each pixel BA. It was found in previous research that the difference between BA and real events is that BA lacks correlation with other BAs in its spatiotemporal neighborhood. The spatiotemporal correlation of two events \(e_{1}\ (x_{1},y_{1},t_{1})\), \(e_{2}\ (x_{2},y_{2},t_{2})\) can be expressed as:  
\[\text{{spatiotemporal correlation}}\Leftrightarrow\left\{ \begin{array}{l}|x_{1}- x_{2}|\leq dN\\ |y_{1}-y_{2}|\leq dN\\ |t_{1}-t_{2}|\leq dt\end{array} \right.. \tag{2}\]  
In Equation (2), \(dN\) is the spatial neighborhood size, and \(dt\) is the time neighborhood size. Based on this characteristic, many filtering methods are designed, and several related works are introduced below. [PERSON] proposed the background activity filter in [22]. This filter will filter out events that have no events in the surrounding 8 pixels in the past time T. Although BA is sparse compared to real events, there is a possibility for two BAs to be close enough in spatiotemporal neighborhood; in this case, the filter passes the latter BA. [PERSON]'s filter groups pixels by subsampling [18]; the subsampling factor is S, so S\({}^{2}\) pixels are a group. The filter determines the time correlation of events in this group, that is, determines whether the time difference between events is less than T; if, yes, passes, otherwise filter. [PERSON]'s filter uses a specific storage method to store the timestamps of events; two 32-bit storage units are used in every row and column to store the coordinates, polarity, and timestamp of the latest event. For example, event \(e_{0}\ (x_{0},y_{0},t_{0},p_{0})\), \(y_{0},p_{0}\), \(t_{0}\) are stored in the storage unit of \(x_{0}\), \(x_{0}\), \(p_{0}\), \(t_{0}\) are stored in the storage unit of \(y_{0}\). This way saves storage space. However, this storage method can only recover events of up to 6 pixels, and more importantly, these 6 pixels are not necessarily neighboring pixels of the newly arrived event pixels. For example, the event sequence is \(e_{1}\ (x,y,t_{1},\ p)\), \(e_{2}\ (x+2,y,t_{2},p)\), \(e_{3}\ (x,y+2,t_{3},p)\), \(e_{4}\ (x,y,t_{4},p)\), where \(t_{1}<t_{2}<t_{3}<t_{4}\), and \(t_{4}-t_{1}<\text{T}\). In this case, according to the coordinates of \(e_{4}\), only the addresses of \(e_{2}\) and \(e_{3}\) can be recovered; \(e_{4}\) is not adjacent to them and will be filtered. However, \(e_{4}\) and \(e_{1}\) have spatiotemporal correlation and should not be filtered. So this will lose more real events. In the study of filter performance evaluation, [PERSON] proposed a method of repeated recording using a fixed pattern to determine whether the event is noise or a real event, thereby evaluating the performance of the filter [4].