Human Detection

Human detection research focuses on developing robust and accurate methods for identifying humans in diverse visual and sensor data, aiming to improve applications ranging from search and rescue to autonomous driving. Current research emphasizes improving the robustness of Convolutional Neural Networks (CNNs), particularly YOLO variants, and exploring alternative modalities like 4D radar and thermal imaging, often employing data augmentation and fusion techniques to handle challenging conditions such as occlusion, poor lighting, and adverse weather. These advancements have significant implications for safety-critical applications and contribute to a broader understanding of computer vision challenges in real-world scenarios.