Hossain, Sadat
The Second Monocular Depth Estimation Challenge
Spencer, Jaime, Qian, C. Stella, Trescakova, Michaela, Russell, Chris, Hadfield, Simon, Graf, Erich W., Adams, Wendy J., Schofield, Andrew J., Elder, James, Bowden, Richard, Anwar, Ali, Chen, Hao, Chen, Xiaozhi, Cheng, Kai, Dai, Yuchao, Hoa, Huynh Thai, Hossain, Sadat, Huang, Jianmian, Jing, Mohan, Li, Bo, Li, Chao, Li, Baojun, Liu, Zhiwen, Mattoccia, Stefano, Mercelis, Siegfried, Nam, Myungwoo, Poggi, Matteo, Qi, Xiaohua, Ren, Jiahui, Tang, Yang, Tosi, Fabio, Trinh, Linh, Uddin, S. M. Nadim, Umair, Khan Muhammad, Wang, Kaixuan, Wang, Yufei, Wang, Yixing, Xiang, Mochu, Xu, Guangkai, Yin, Wei, Yu, Jun, Zhang, Qi, Zhao, Chaoqiang
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC). This edition was open to methods using any form of supervision, including fully-supervised, self-supervised, multi-task or proxy depth. The challenge was based around the SYNS-Patches dataset, which features a wide diversity of environments with high-quality dense ground-truth. This includes complex natural environments, e.g. forests or fields, which are greatly underrepresented in current benchmarks. The challenge received eight unique submissions that outperformed the provided SotA baseline on any of the pointcloud- or image-based metrics. The top supervised submission improved relative F-Score by 27.62%, while the top self-supervised improved it by 16.61%. Supervised submissions generally leveraged large collections of datasets to improve data diversity. Self-supervised submissions instead updated the network architecture and pretrained backbones. These results represent a significant progress in the field, while highlighting avenues for future research, such as reducing interpolation artifacts at depth boundaries, improving self-supervised indoor performance and overall natural image accuracy.
Crime Prediction Using Multiple-ANFIS Architecture and Spatiotemporal Data
Islam, Mashnoon, Karim, Redwanul, Roy, Kalyan, Mahmood, Saif, Hossain, Sadat, Rahman, M. Rashedur
Statistical values alone cannot bring the whole scenario of crime occurrences in the city of Dhaka. We need a better way to use these statistical values to predict crime occurrences and make the city a safer place to live. Proper decision-making for the future is key in reducing the rate of criminal offenses in an area or a city. If the law enforcement bodies can allocate their resources efficiently for the future, the rate of crime in Dhaka can be brought down to a minimum. In this work, we have made an initiative to provide an effective tool with which law enforcement officials and detectives can predict crime occurrences ahead of time and take better decisions easily and quickly. We have used several Fuzzy Inference Systems (FIS) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to predict the type of crime that is highly likely to occur at a certain place and time.