Low-light Environment Neural Surveillance

Furthermore, the rate of reported crimes is dependent on the victims or bystanders to self-report. We design and implement an end-to-end system for real-time Though there exist algorithms for fully automated action crime detection in low-light environments. Unlike Closed-recognition [1-5], many are not applied in real-time Circuit Television, which performs reactively, the Low-Light or low-light environments. The existing benchmark action Environment Neural Surveillance provides real time crime recognition datasets such as HMDB-51 [6] (Human Motion alerts. The system uses a low-light video feed processed DataBase), UCF-101 [7] (University of Central Florida), in real-time by an optical-flow network, spatial and temporal and Sports-1M [8] contain primarily daytime videos. UCF networks, and a Support Vector Machine to identify released the UCF-Crime dataset [9] for general anomaly shootings, assaults, and thefts. We create a low-light actionrecognition detection and recognizes 13 crime categories, including arrest, dataset, LENS-4, which will be publicly available.