Real-time Topology-based Tracking of Microscopic Objects

Visual tracking of microscopic objects is one of the most important studies of dynamic biological processes and requires automated segmentation and tracking methods. It is often limited to the morphology of objects or human study and lacks the automation and scalability to detect objects, track the path of any object, and examine their topology with the detection of related anomalies. This paper presents a fast scalable agent-oriented method for automatic detection, real-time video tracking, simultaneous tracking of microscopic objects, monitoring object behavior, and their topology based on graph theory applicable to the Internet of Things. It has no mentioned restrictions. Its segmentation method is a combination of temporal and spatial changes of the image to detect moving objects and predict their movement path, and the possibility of detecting individual anomalies of the object (death, moving a stop, collision of objects, a sudden departure from and a sudden entry into processing frame). Provides abrupt onset and onset of anomalies (network splitting, batch changes, batch decomposition, batch spacing, attenuation, and network collapse). The results of experimental experiments to track microscopic objects of sperm and birds in 2D images of 3D video film show that it has 99% sensitivity and 97% accuracy of instantaneous detection of objects with 99% detection accuracy. In monitoring and tracking, correlation and collision of sperm objects have an accuracy of 99.8% and in birds due to environmental noise and error detection in rapid topology changes, birds have an accuracy of 88%.