Multi Object Tracking for Similar Instances: A Hybrid Architecture

Tracking and segmentation of moving objects in videos continues to be the central problem in the separation and prediction of concurrent episodes and situation understanding. Along with critical issues such as collision avoidance, tracking and segmentation have numerous applications in other disciplines, including medicine research. To infer the potential side effects of a given treatment, behaviour analysis of la-boratory animals should be performed, which can be achieved via tracking. This presents a difficult task due to the special circumstances, such as the highly similar shape and the unpredictable movement of the subject creatures, but a precise solution would accelerate research by eliminating the need of manual supervision. To this end, we propose Cluster R-CNN, a deep architecture that uses clustering to segment object instances in a given image and track them across subsequent frames. We show that pairwise clustering coupled with a recurrent unit successfully extends Mask R-CNN to a model capable of tracking and segmenting highly similar moving and occluded objects, providing proper results even in certain cases where related networks fail. In addition to theoretical background and reasoning, our work also features experiments on a unique rat tracking data set, with quantitative results to compare the aforementioned model with other architectures. The proposed Cluster R-CNN serves as a baseline for future work towards achieving an automatic monitoring tool for biomedical research.