Multi Target Tracking

Multi-target tracking (MTT) focuses on estimating the positions and identities of multiple objects over time, a crucial task across diverse fields. Current research emphasizes robust algorithms that handle occlusions, sensor noise, and dynamic environments, often employing particle filters, Bayesian methods, neural networks (including transformers and convolutional neural networks), and graph-based approaches like belief propagation and minimum cost flow. These advancements are improving the accuracy and efficiency of MTT in applications ranging from autonomous vehicle navigation and surveillance to robotics and biological research, particularly in challenging scenarios with limited or unreliable sensing.