A multi-scale video surveillance based information aggregation model for crime prediction

When studying the relationship and potential association between various data, researchers face multiple challenges: first, researchers need to collect data from multiple knowledge systems and channels, which challenges the breadth of expertise of researchers; second, it is challenging to perceive the changes of application scenarios and environment through these data such as image unstructured data that may contain useful information; third, short term behavior prediction with more frequent changes is challenging, especially when the sensitivity of the prediction model is insufficient and there is a lack of support from other feature data. This article designs a multi-scale information aggregation prediction model based on video surveillance for predicting and analyzing short-term time series with less data, such as criminal behavior. Firstly, a multi-scale human traffic perception and quantification model was established based on FairMOT to obtain human traffic time series data, which is an important association to strengthen the main features of subsequent short-term time series. Then, an improved prediction model, the Anti Saturation Gate Control Model (ASGCM), is designed to introduce entry-level techniques and add anti-saturation conversion modules, making ASGCM more sensitive, helping to reduce dependence on long-term features, and suppressing gradient disappearance and exploration problems. Finally, the application of urban crime prediction was used as an example for validation, and two sets of data were tested using a single sequence and a fused sequence, respectively. The results showed that the ASGCM method proposed in this paper can improve prediction accuracy while reducing computational complexity.