## 4 Experimentation
### Experiment Results and Ablation Study
#### 4.4.5 Analysis of Computational Cost of The Proposed DMRMs  
All the experiments are performed on the Ubuntu 16.04 LTS system with an Intel Core i7 processor along with the NVIDIA GeForce GTX1080 Ti GPU. In Table 7, we have demonstrated the computational cost of the proposed anomalous event detector. We achieve 26 frames per second (FPS) and 18 FPS on UCF-Crime and ShanghaiTech datasets, respectively. As the size of each frame is 856 \(\times\) 480 and 320 \(\times\) 240 in ShanghaiTech and UCF-Crime datasets, respectively, therefore, the computational cost of extracting motion flow maps is higher in ShanghaiTech dataset than that of the UCF-Crime dataset, which affects the overall FPS of the anomalous event detector's computational cost. features, our framework, DMRMs, competitively learned the context-dependency compared to other deep learning algorithms [1, 3, 12, 13]. We achieved promising performance on challenging datasets. The experimental results demonstrated noticeable improvement in context-learning, mitigating the false alarm rate, and overall accuracy in the anomalous event detection task. However, there are several drawbacks to our method, which should be handled in the future. First, a large-scale and well-annotated dataset for both normal events and abnormal events is necessary to train the proposed framework since it performs in a semi-supervised manner. A large-scale dataset requirement is an inherent problem in almost all existing visual recognition methods using deep learning. Second, our framework is not robust to the noises (occlusion, camera jitters, and illumination variations). Therefore, our future work will focus on training our network in an unsupervised manner using a smaller amount of training data; consequently, this will address the problem of the requirement of a large-scale and well-annotated dataset. Moreover, our future work will also focus on overcoming the shortcomings of noise due to illumination variations, camera jitters, and occlusion. Finally, we note that in the ShanghaiTech dataset, some algorithms outperform our proposed model on the given experimental setting. However, performance improvement is still possible by adopting different models (e.g., 3D ResNet-50) and their optimization by retraining (with practically many training samples), fine-grained transfer learning, and knowledge distillation, and this is our ongoing work. Conceptualization, S.D. and M.J.; methodology, S.D., J.G. and M.J.; software, S.D. ; validation, S.D. and A.B. ; formal analysis, S.D., A.B. and J.G. ; investigation, J.G. and M.J.; resources, M.J.; data curation, S.D. ; writing--original draft preparation, S.D., and M.J.; writing--review and editing, J.G. and M.J.; visualization, S.D. ; supervision, M.G. ; project administration, M.J.; funding acquisition, J.G and M.J. All authors have read and agreed to the published version of the manuscript. This work was partly supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2014-3-00077, Development of Global Multitarget Tracking and Event Prediction Techniques Based on Real-time Large-Scale Video Analysis), Ministry of Culture, Sports and Tourism (MCST) Korea Creative Content Agency (KOCCA) in the Culture Technology (CT) Research & Development (R2020070004) Program 2021, and the Brain Research Program through the NRF funded by the Ministry of Science, ICT and Future Planning (Grant No. NRF-2019M3C7A1020406). Not applicable. Data Availability Statement: Data are available in publicly accessible repositories. Publicly available datasets were analyzed in this study.