A unified multi-task learning architecture for fast and accurate pedestrian detection

• Main Authors: Zhou, Chengju, Wu, Meiqing, Lam, Siew-Kei
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering; Multi-Task Learning; Pedestrian Detection; Semantic Segmentation; Feature Aggregation
• Online Access: https://hdl.handle.net/10356/147488
• Institution: Nanyang Technological University

We present a unified multi-task learning architecture for fast and accurate pedestrian detection. Different from existing methods which often focus on either a new loss function or architecture, we propose an improved multi-task convolutional neural network learning architecture to effectively and...