Deep Tracker

Deep trackers utilize deep learning to locate and track objects in video sequences, aiming for high accuracy and efficiency across diverse scenarios. Current research emphasizes improving robustness to occlusions, variations in lighting and appearance, and computational constraints, often employing Siamese networks, transformers, and correlation filters within a variety of architectures. These advancements are crucial for applications ranging from autonomous driving and robotics to medical image analysis and high-throughput phenotyping, where accurate and real-time object tracking is essential. Furthermore, research is actively exploring methods to improve the interpretability and robustness of these trackers against adversarial attacks.