Real-World Anomaly Detection Using Deep Learning Unnati Koppikar, C. Sujatha, Prakashgoud Patil and Uma Mudenagudi Abstract In this paper, we have carried out a comparative study on two deep learning models for detecting real-world anomalies in surveillance videos. Anomalous event is the one which deviates from the normal behavior. The anomalies considered are related to thefts such as robbery, burglary, stealing, and shoplifting. A framework is set up using supervised learning approach to train the models using the weakly labeled videos. The deep learning models considered are VGG-16 and inception model which are trained with both anomalous and normal videos to detect any anomalous activity in the video frame. UCF-Crime dataset is used which comprises long, untrimmed surveillance videos. The deep learning models are evaluated using various metrics. The experimental results show that the Inception V3 model performs significantly better in detecting the anomalies as compared to the VGG-16 model with an accuracy of 94.54%. Keywords Surveillance · Anomaly detection · Theft · Deep learning · Convolutional Neural Networks · VGG-16 model · Inception V3 model U. Koppikar (B ) · C. Sujatha (B ) Department of CSE, KLE Technological University, Hubballi, India e-mail: unnatikoppikar@gmail.com C. Sujatha e-mail: sujata_c@kletech.ac.in P. Patil Department of MCA, KLE Technological University, Hubballi, India e-mail: prpatilji@gmail.com U. Mudenagudi Department of ECE, KLE Technological University, Hubballi, India e-mail: uma@kletech.ac.in © Springer Nature Singapore Pte Ltd. 2020 V. Bhateja et al. (eds.), Intelligent Computing and Communication, Advances in Intelligent Systems and Computing 1034, https://doi.org/10.1007/978-981-15-1084-7_32 333