Qing Tian

North China University of Technology, Nanjing University of Information Science and Technology

Papers

Total Citations

H-Index

About

Qing Tian is a researcher whose work bridges computer vision and natural language processing, with a particular focus on pedestrian detection and conversational AI. In their highly cited 2013 paper, "An Improved Pedestrian Detection Algorithm Integrating Haar-Like Features and HOG Descriptors" (33 citations), Tian introduced a novel fusion of Haar-like features, the AdaBoost algorithm, and histogram of oriented gradients (HOG) descriptors. This approach significantly enhanced detection accuracy in complex environments, advancing applications in intelligent surveillance and autonomous robotics. Building on this foundation, Tian later explored human-computer interaction through "A Hybrid Chinese Conversation Model based on Retrieval and Generation" (2020, 6 citations), which combined retrieval-based and generative methods to improve dialogue fluency. While the citation count for this work is modest, it reflects Tian's versatility in tackling diverse challenges—from visual perception to language understanding. Their contributions to pedestrian detection remain a cornerstone for researchers developing safer, more responsive autonomous systems, demonstrating a commitment to practical, real-world impact.