CrimeAlarm: Towards Intensive Intent Dynamics in Fine-grained Crime Prediction

arXiv:2404.06756v1 Announce Type: new
Abstract: Granularity and accuracy are two crucial factors for crime event prediction. Within fine-grained event classification, multiple criminal intents may alternately exhibit in preceding sequential events, and progress differently in next. Such intensive intent dynamics makes training models hard to capture unobserved intents, and thus leads to sub-optimal generalization performance, especially in the intertwining of numerous potential events. To capture comprehensive criminal intents, this paper proposes a fine-grained sequential crime prediction framework, CrimeAlarm, that equips with …

abstract accuracy arxiv classification crime cs.ai cs.lg dynamics event events fine-grained leads multiple next prediction progress training training models type