Social and Behavioral Sciences Commons^™

Human Suspicious Activity Detection, Nilamben Bhuva

Electronic Theses, Projects, and Dissertations

The detection of suspicious human activity is a crucial aspect of ensuring public safety and security. The aim is to identify suspicious behavior. To accomplish this, we employ the LRCN, a long-term recurrent convolutional network, to detect anomalous activity. It is important to consider the temporal data of the video when classifying suspicious behavior, and the framework uses a combination of CNNs and RNNs to analyze video frames and extract relevant features. The key milestones of this project include conducting research, collecting and pre-processing data, designing and training the model, and evaluating its performance. The resulting detection system can accurately …