Automated Behavioral Analysis Using Instance Segmentation

Animal behavior analysis plays a crucial role in various fields, such as life science and biomedical research. However, the scarcity of available data and the high cost associated with obtaining a large number of labeled datasets pose significant challenges. In this research, we propose a novel approach that leverages instance segmentation-based transfer learning to address these issues. By capitalizing on fine-tuning the classification head of the instance segmentation network, we enable the tracking of multiple animals and facilitate behavior analysis in laboratory-recorded videos. To demonstrate the effectiveness of our method, we conducted a series of experiments, revealing that our approach achieves exceptional performance levels, comparable to human capabilities, across a diverse range of animal behavior analysis tasks. Moreover, we emphasize the practicality of our solution, as it requires only a small number of labeled images for training. To facilitate the adoption and further development of our method, we have developed an open-source implementation named Annolid (An annotation and instance segmentation-based multiple animal tracking and behavior analysis package). The codebase is publicly available on GitHub at https://github.com/cplab/annolid. This resource serves as a valuable asset for researchers and practitioners interested in advancing animal behavior analysis through state-of-the-art techniques

An algorithm using YOLOv4 and DeepSORT for tracking vehicle speed on highway

Currently, expressways are increasingly developed and expanded. Several highways of Vietnam allow vehicles to travel up to 120 kilometers per hour helping to transport goods quickly and bring a lot of socio-economic benefits. Vehicle monitoring plays an important role in reducing traffic accidents helping to handle violations.The paper proposes a model to identify and monitor car speed on highways. The proposal method uses YOLOv4 combining with DeepSORT for vehicle identification and tracking. We then calculate the speed of car based on video recording and sending back from highway. The execution context is highway where vehicles move very fast. The results show that system meets set requirements with over 90% accuracy and execution times for up to 70 frames per second that is suitable for real systems